
'''simple docstring'''




import argparse

import torch

from transformers import MraConfig, MraForMaskedLM






def     snake_case__							(      _A: Tuple			)   ->     int:

		'''simple docstring'''


		if "model" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""model."""				,       """"""			)
		if "norm1" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""norm1"""				,       """attention.output.LayerNorm"""			)
		if "norm2" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""norm2"""				,       """output.LayerNorm"""			)
		if "norm" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""norm"""				,       """LayerNorm"""			)
		if "transformer" in orig_key:
				lowerCAmelCase        =  orig_key.split("""."""			)[0].split("""_"""			)[-1]
				lowerCAmelCase        =  orig_key.replace(f"transformer_{layer_num}"				,       f"encoder.layer.{layer_num}"			)
		if "mha.attn" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""mha.attn"""				,       """attention.self"""			)
		if "mha" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""mha"""				,       """attention"""			)
		if "W_q" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""W_q"""				,       """self.query"""			)
		if "W_k" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""W_k"""				,       """self.key"""			)
		if "W_v" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""W_v"""				,       """self.value"""			)
		if "ff.0" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""ff.0"""				,       """intermediate.dense"""			)
		if "ff.2" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""ff.2"""				,       """output.dense"""			)
		if "ff" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""ff"""				,       """output.dense"""			)
		if "mlm_class" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""mlm.mlm_class"""				,       """cls.predictions.decoder"""			)
		if "mlm" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""mlm"""				,       """cls.predictions.transform"""			)
		if "backbone.backbone.encoders" in orig_key:
				lowerCAmelCase        =  orig_key.replace("""backbone.backbone.encoders"""				,       """encoder.layer"""			)
		if "cls" not in orig_key:
				lowerCAmelCase        =  """mra.""" + orig_key

		return orig_key






def     snake_case__							(      _A: int				,       _A: Tuple			)   ->     Optional[Any]:

		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				lowerCAmelCase        =  orig_state_dict.pop(_A			)

				if ("pooler" in key) or ("sen_class" in key):
						continue
				else:
						lowerCAmelCase        =  val

		lowerCAmelCase        =  orig_state_dict["""cls.predictions.decoder.bias"""]
		lowerCAmelCase        =  torch.arange(_A			).expand((1, -1)			) + 2

		return orig_state_dict






def     snake_case__							(      _A: str				,       _A: Union[str, Any]				,       _A: int			)   ->     Optional[Any]:

		'''simple docstring'''


		lowerCAmelCase        =  torch.load(_A				,       map_location="""cpu"""			)["""model_state_dict"""]
		lowerCAmelCase        =  MraConfig.from_json_file(_A			)
		lowerCAmelCase        =  MraForMaskedLM(_A			)

		lowerCAmelCase        =  convert_checkpoint_helper(config.max_position_embeddings				,       _A			)

		print(model.load_state_dict(_A			)			)
		model.eval()
		model.save_pretrained(_A			)

		print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"			)


if __name__ == "__main__":
						__lowercase				   =  argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Mra pytorch checkpoint.'''
						)
						parser.add_argument(
						    '''--config_file''',
						    default=None,
						    type=str,
						    required=True,
						    help='''The json file for Mra model config.''',
						)
						parser.add_argument(
						    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
						)
						__lowercase				   =  parser.parse_args()
						convert_mra_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





'''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     snake_case__							(      _A: Dict				,       _A: str				,       _A: int			)   ->     Any:

		'''simple docstring'''


		lowerCAmelCase        =  FNetConfig.from_json_file(_A			)
		print(f"Building PyTorch model from configuration: {config}"			)
		lowerCAmelCase        =  FNetForPreTraining(_A			)

		lowerCAmelCase        =  restore_checkpoint(_A				,       _A			)
		lowerCAmelCase        =  checkpoint_dict["""target"""]

		# Embeddings
		# Position IDs
		lowerCAmelCase        =  fnet_pretraining_model.state_dict()

		lowerCAmelCase        =  state_dict["""fnet.embeddings.position_ids"""]
		lowerCAmelCase        =  {"""fnet.embeddings.position_ids""": position_ids}
		# Embedding Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]			)

		# Encoder Layers
		for layer in range(config.num_hidden_layers			):
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""mixing_layer_norm"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""intermediate"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""kernel"""]			).T
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"feed_forward_{layer}"]["""output"""]["""bias"""]			)

				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""scale"""]			)
				lowerCAmelCase        =  torch.tensor(
				    pretrained_model_params["""encoder"""][f"encoder_{layer}"]["""output_layer_norm"""]["""bias"""]			)

		# Pooler Layers
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]			)

		# Masked LM Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""kernel"""]			).T
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_dense"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]			)
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""predictions_output"""]["""output_bias"""]			)
		lowerCAmelCase        =  torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]			)

		# Seq Relationship Layers
		lowerCAmelCase        =  torch.tensor(
		    pretrained_model_params["""classification"""]["""output_kernel"""]			)
		lowerCAmelCase        =  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__":
						__lowercase				   =  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.''')
						__lowercase				   =  parser.parse_args()
						convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




