





'''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							,      UpperCAmelCase							,      UpperCAmelCase     )   ->List[Any]:



	"""simple docstring"""







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

	A					=   restore_checkpoint(UpperCAmelCase							,      UpperCAmelCase     )
	A					=   checkpoint_dict["""target"""]

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

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

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

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

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

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

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

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

	# Seq Relationship Layers
	A					=   torch.tensor(
	    pretrained_model_params["""classification"""]["""output_kernel"""]     )
	A					=   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					:						Optional[int]					     =							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					:						Dict					     =							parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)








'''simple docstring'''


import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:						Dict					     =							logging.get_logger(__name__)

_lowerCamelCase					:						List[Any]					     =							['UtteranceLevel']




@torch.no_grad()
def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   torch.load(UpperCAmelCase							,      map_location="""cpu"""     )
	if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
		raise NotImplementedError(f"""The supported s3prl models are {SUPPORTED_MODELS}"""     )

	A					=   checkpoint["""Downstream"""]

	A					=   HubertConfig.from_pretrained(UpperCAmelCase     )
	A					=   HubertForSequenceClassification.from_pretrained(UpperCAmelCase							,      config=UpperCAmelCase     )
	A					=   WavaVecaFeatureExtractor.from_pretrained(
	    UpperCAmelCase							,      return_attention_mask=UpperCAmelCase							,      do_normalize=UpperCAmelCase     )

	if hf_congfig.use_weighted_layer_sum:
		A					=   checkpoint["""Featurizer"""]["""weights"""]

	A					=   downstream_dict["""projector.weight"""]
	A					=   downstream_dict["""projector.bias"""]
	A					=   downstream_dict["""model.post_net.linear.weight"""]
	A					=   downstream_dict["""model.post_net.linear.bias"""]

	hf_feature_extractor.save_pretrained(UpperCAmelCase     )
	hf_model.save_pretrained(UpperCAmelCase     )


if __name__ == "__main__":
				_lowerCamelCase					:						List[str]					     =							argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				_lowerCamelCase					:						List[Any]					     =							parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

