





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
snake_case_				=      logging.get_logger(__name__)

snake_case_				=      {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''mask_emb''': '''masked_spec_embed''',
}


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  str:
		for attribute in key.split('''.'''		):
				lowerCamelCase :			str    		=getattr(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)

		if weight_type is not None:
				lowerCamelCase :			Dict    		=getattr(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		).shape
		else:
				lowerCamelCase :			int    		=hf_pointer.shape

		assert hf_shape == value.shape, (
		    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
		    F" {value.shape} for {full_name}"
		)

		if weight_type == "weight":
				lowerCamelCase :			List[Any]    		=value
		elif weight_type == "weight_g":
				lowerCamelCase :			Optional[int]    		=value
		elif weight_type == "weight_v":
				lowerCamelCase :			List[Any]    		=value
		elif weight_type == "bias":
				lowerCamelCase :			Any    		=value
		else:
				lowerCamelCase :			List[Any]    		=value

		logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Union[str, Any]:
		lowerCamelCase :			Any    		=[]
		lowerCamelCase :			Any    		=fairseq_model.state_dict()

		lowerCamelCase :			Dict    		=hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				lowerCamelCase :			Any    		=False
				if "conv_layers" in name:
						load_conv_layer(
						    SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							hf_model.config.feat_extract_norm == '''group'''				,							)
						lowerCamelCase :			str    		=True
				else:
						for key, mapped_key in MAPPING.items():
								lowerCamelCase :			Any    		=mapped_key

								if key in name:
										lowerCamelCase :			Tuple    		=True
										if "*" in mapped_key:
												lowerCamelCase :			Optional[Any]    		=name.split(SCREAMING_SNAKE_CASE_		)[0].split('''.'''		)[-2]
												lowerCamelCase :			Optional[int]    		=mapped_key.replace('''*'''				,							SCREAMING_SNAKE_CASE_		)
										if "weight_g" in name:
												lowerCamelCase :			Union[str, Any]    		='''weight_g'''
										elif "weight_v" in name:
												lowerCamelCase :			int    		='''weight_v'''
										elif "weight" in name:
												lowerCamelCase :			Union[str, Any]    		='''weight'''
										elif "bias" in name:
												lowerCamelCase :			List[Any]    		='''bias'''
										else:
												lowerCamelCase :			Optional[Any]    		=None
										set_recursively(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
								continue
				if not is_used:
						unused_weights.append(SCREAMING_SNAKE_CASE_		)

		logger.warning(F"Unused weights: {unused_weights}"		)


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  Optional[int]:
		lowerCamelCase :			List[str]    		=full_name.split('''conv_layers.'''		)[-1]
		lowerCamelCase :			int    		=name.split('''.'''		)
		lowerCamelCase :			Tuple    		=int(items[0]		)
		lowerCamelCase :			Tuple    		=int(items[1]		)

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    F"{full_name} has size {value.shape}, but"
						    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
						)
						lowerCamelCase :			str    		=value
						logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    F"{full_name} has size {value.shape}, but"
						    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
						)
						lowerCamelCase :			str    		=value
						logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."		)
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
						    " found."
						)
						lowerCamelCase :			Optional[int]    		=value
						logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    F"{full_name} has size {value.shape}, but"
						    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
						)
						lowerCamelCase :			Union[str, Any]    		=value
						logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."		)
		else:
				unused_weights.append(SCREAMING_SNAKE_CASE_		)


def        A__						(							SCREAMING_SNAKE_CASE_		) ->  Optional[Any]:
		lowerCamelCase :			str    		=HubertConfig()
		lowerCamelCase :			List[Any]    		=model.config

		lowerCamelCase :			Tuple    		=fs_config.activation_dropout
		lowerCamelCase :			Optional[int]    		=False
		lowerCamelCase :			Any    		=fs_config.attention_dropout
		lowerCamelCase :			str    		=False
		lowerCamelCase :			Any    		=eval(fs_config.extractor_conv_feature_layers		)
		lowerCamelCase :			Dict    		=[x[0] for x in conv_layers]
		lowerCamelCase :			Union[str, Any]    		=[x[1] for x in conv_layers]
		lowerCamelCase :			List[str]    		=[x[2] for x in conv_layers]
		lowerCamelCase :			Union[str, Any]    		='''gelu'''
		lowerCamelCase :			Tuple    		='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
		lowerCamelCase :			Dict    		=False
		lowerCamelCase :			Tuple    		=0.0
		lowerCamelCase :			Optional[Any]    		=0.0
		lowerCamelCase :			List[Any]    		=fs_config.activation_fn
		lowerCamelCase :			Any    		=fs_config.dropout
		lowerCamelCase :			int    		=fs_config.encoder_embed_dim
		lowerCamelCase :			List[Any]    		=0.0_2
		lowerCamelCase :			Tuple    		=fs_config.encoder_ffn_embed_dim
		lowerCamelCase :			Union[str, Any]    		=1E-5
		lowerCamelCase :			Any    		=0.0
		lowerCamelCase :			Any    		=fs_config.encoder_attention_heads
		lowerCamelCase :			Any    		=fs_config.conv_pos_groups
		lowerCamelCase :			Optional[int]    		=fs_config.conv_pos
		lowerCamelCase :			Union[str, Any]    		=len(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[Any]    		=fs_config.encoder_layers

		return config


@torch.no_grad()
def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_=None		) ->  List[Any]:
		lowerCamelCase :			List[Any]    		=distilhubert().model.model

		if config_path is not None:
				lowerCamelCase :			Union[str, Any]    		=HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE_		)
		else:
				lowerCamelCase :			Any    		=convert_config(SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			str    		=model.eval()

		lowerCamelCase :			List[Any]    		=WavaVecaFeatureExtractor(
		    feature_size=1				,							sampling_rate=1_6_0_0_0				,							padding_value=0				,							do_normalize=SCREAMING_SNAKE_CASE_				,							return_attention_mask=SCREAMING_SNAKE_CASE_				,							)
		lowerCamelCase :			Union[str, Any]    		=HubertModel(SCREAMING_SNAKE_CASE_		)

		recursively_load_weights(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)

		feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_		)
		hf_model.save_pretrained(SCREAMING_SNAKE_CASE_		)


if __name__ == "__main__":
				snake_case_				=      argparse.ArgumentParser()
				parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
				parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
				snake_case_				=      parser.parse_args()
				convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








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__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		) ->  List[Any]:
		# Initialise PyTorch model
		lowerCamelCase :			Dict    		=FNetConfig.from_json_file(SCREAMING_SNAKE_CASE_		)
		print(F"Building PyTorch model from configuration: {config}"		)
		lowerCamelCase :			Tuple    		=FNetForPreTraining(SCREAMING_SNAKE_CASE_		)

		lowerCamelCase :			List[Any]    		=restore_checkpoint(SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_		)
		lowerCamelCase :			List[str]    		=checkpoint_dict['''target''']

		# Embeddings
		# Position IDs
		lowerCamelCase :			Optional[Any]    		=fnet_pretraining_model.state_dict()

		lowerCamelCase :			Tuple    		=state_dict['''fnet.embeddings.position_ids''']
		lowerCamelCase :			str    		={'''fnet.embeddings.position_ids''': position_ids}
		# Embedding Layers
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''word''']['''embedding''']		)
		lowerCamelCase :			int    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''position''']['''embedding'''][0]		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''type''']['''embedding''']		)
		lowerCamelCase :			str    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''kernel''']		).T
		lowerCamelCase :			List[Any]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''hidden_mapping_in''']['''bias''']		)
		lowerCamelCase :			List[str]    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''scale''']		)
		lowerCamelCase :			Tuple    		=torch.tensor(
		    pretrained_model_params['''encoder''']['''embedder''']['''layer_norm''']['''bias''']		)

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

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

				lowerCamelCase :			str    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''kernel''']		).T
				lowerCamelCase :			Tuple    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"feed_forward_{layer}"]['''output''']['''bias''']		)

				lowerCamelCase :			List[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''scale''']		)
				lowerCamelCase :			Optional[Any]    		=torch.tensor(
				    pretrained_model_params['''encoder'''][F"encoder_{layer}"]['''output_layer_norm''']['''bias''']		)

		# Pooler Layers
		lowerCamelCase :			Any    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''kernel''']		).T
		lowerCamelCase :			str    		=torch.tensor(pretrained_model_params['''encoder''']['''pooler''']['''bias''']		)

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

		# Seq Relationship Layers
		lowerCamelCase :			Union[str, Any]    		=torch.tensor(
		    pretrained_model_params['''classification''']['''output_kernel''']		)
		lowerCamelCase :			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__":
				snake_case_				=      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.''')
				snake_case_				=      parser.parse_args()
				convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)

