
"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__				:   Tuple   							=			logging.get_logger(__name__)

UpperCAmelCase__				:   List[str]   							=			{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    'encoder.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case       ,_snake_case						):





							for attribute in key.split("""."""						):
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       getattr(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__						)

							if weight_type is not None:
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       getattr(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__						).shape
							else:
														SCREAMING_SNAKE_CASE__						:	str									=       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":
														SCREAMING_SNAKE_CASE__						:	Any									=       value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE__						:	Tuple									=       value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       value
							else:
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       value

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

def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       []
							SCREAMING_SNAKE_CASE__						:	Any									=       fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														SCREAMING_SNAKE_CASE__						:	Optional[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"""       ,)
																					SCREAMING_SNAKE_CASE__						:	Optional[int]									=       True
														else:
																					for key, mapped_key in MAPPING.items():
																												SCREAMING_SNAKE_CASE__						:	str									=       'sew_d.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

																												if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
																																			SCREAMING_SNAKE_CASE__						:	str									=       True
																																			if "*" in mapped_key:
																																										SCREAMING_SNAKE_CASE__						:	Dict									=       name.split(SCREAMING_SNAKE_CASE__						)[0].split("""."""						)[-2]
																																										if not layer_index.isnumeric():
																																																	continue
																																										SCREAMING_SNAKE_CASE__						:	str									=       mapped_key.replace("""*"""       ,SCREAMING_SNAKE_CASE__						)
																																			if "weight_g" in name:
																																										SCREAMING_SNAKE_CASE__						:	Tuple									=       'weight_g'
																																			elif "weight_v" in name:
																																										SCREAMING_SNAKE_CASE__						:	List[Any]									=       'weight_v'
																																			elif "weight" in name:
																																										SCREAMING_SNAKE_CASE__						:	Any									=       'weight'
																																			elif "bias" in name:
																																										SCREAMING_SNAKE_CASE__						:	str									=       'bias'
																																			else:
																																										SCREAMING_SNAKE_CASE__						:	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 lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	List[str]									=       full_name.split("""conv_layers."""						)[-1]
							SCREAMING_SNAKE_CASE__						:	Tuple									=       name.split("""."""						)
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       int(items[0]						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       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.'''
																					)
																					SCREAMING_SNAKE_CASE__						:	List[Any]									=       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.'''
																					)
																					SCREAMING_SNAKE_CASE__						:	Any									=       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."
																					)
																					SCREAMING_SNAKE_CASE__						:	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.'''
																					)
																					SCREAMING_SNAKE_CASE__						:	int									=       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 lowercase_  (       _snake_case       ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       SEWDConfig()
							if is_finetuned:
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       model.wav_encoder.wav_model.cfg
							else:
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       model.cfg

							SCREAMING_SNAKE_CASE__						:	List[Any]									=       fs_config.conv_bias
							SCREAMING_SNAKE_CASE__						:	int									=       eval(fs_config.conv_feature_layers						)
							SCREAMING_SNAKE_CASE__						:	int									=       [x[0] for x in conv_layers]
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       [x[1] for x in conv_layers]
							SCREAMING_SNAKE_CASE__						:	Dict									=       [x[2] for x in conv_layers]
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       'gelu'
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
							SCREAMING_SNAKE_CASE__						:	List[str]									=       0.0
							SCREAMING_SNAKE_CASE__						:	int									=       fs_config.activation_fn.name
							SCREAMING_SNAKE_CASE__						:	Tuple									=       fs_config.encoder_embed_dim
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       0.02
							SCREAMING_SNAKE_CASE__						:	Dict									=       fs_config.encoder_ffn_embed_dim
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       1E-5
							SCREAMING_SNAKE_CASE__						:	Dict									=       fs_config.encoder_layerdrop
							SCREAMING_SNAKE_CASE__						:	Any									=       fs_config.encoder_attention_heads
							SCREAMING_SNAKE_CASE__						:	int									=       fs_config.conv_pos_groups
							SCREAMING_SNAKE_CASE__						:	Any									=       fs_config.conv_pos
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       len(SCREAMING_SNAKE_CASE__						)
							SCREAMING_SNAKE_CASE__						:	Any									=       fs_config.encoder_layers
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       fs_config.squeeze_factor
							# DeBERTa-specific parameters:
							SCREAMING_SNAKE_CASE__						:	Dict									=       fs_config.max_position_embeddings
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       fs_config.position_buckets
							SCREAMING_SNAKE_CASE__						:	str									=       fs_config.share_att_key
							SCREAMING_SNAKE_CASE__						:	Dict									=       fs_config.relative_attention
							SCREAMING_SNAKE_CASE__						:	Any									=       fs_config.position_biased_input
							SCREAMING_SNAKE_CASE__						:	str									=       tuple(fs_config.pos_att_type.split("""|"""						)						)
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       fs_config.norm_rel_ebd

							# take care of any params that are overridden by the Wav2VecCtc model
							if is_finetuned:
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       model.cfg
														SCREAMING_SNAKE_CASE__						:	List[str]									=       fs_config.final_dropout
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       fs_config.layerdrop
							SCREAMING_SNAKE_CASE__						:	str									=       fs_config.activation_dropout
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       fs_config.attention_dropout
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       fs_config.dropout_input
							SCREAMING_SNAKE_CASE__						:	Tuple									=       fs_config.dropout
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       fs_config.mask_channel_length
							SCREAMING_SNAKE_CASE__						:	Dict									=       fs_config.mask_channel_prob
							SCREAMING_SNAKE_CASE__						:	int									=       fs_config.mask_length
							SCREAMING_SNAKE_CASE__						:	Dict									=       fs_config.mask_prob

							SCREAMING_SNAKE_CASE__						:	Dict									=       'Wav2Vec2FeatureExtractor'
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       'Wav2Vec2CTCTokenizer'

							return config

@torch.no_grad()
def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case=None       ,_snake_case=None       ,_snake_case=True						):





							if is_finetuned:
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]       ,arg_overrides={"""data""": """/""".join(dict_path.split("""/"""						)[:-1]						)}						)
							else:
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]						)

							if config_path is not None:
														SCREAMING_SNAKE_CASE__						:	List[str]									=       SEWDConfig.from_pretrained(SCREAMING_SNAKE_CASE__						)
							else:
														SCREAMING_SNAKE_CASE__						:	Tuple									=       convert_config(model[0]       ,SCREAMING_SNAKE_CASE__						)
							SCREAMING_SNAKE_CASE__						:	List[str]									=       model[0].eval()

							SCREAMING_SNAKE_CASE__						:	List[Any]									=       True if config.feat_extract_norm == 'layer' else False
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       WavaVecaFeatureExtractor(
							    feature_size=1       ,sampling_rate=16_000       ,padding_value=0       ,do_normalize=SCREAMING_SNAKE_CASE__       ,return_attention_mask=SCREAMING_SNAKE_CASE__       ,)

							if is_finetuned:
														if dict_path:
																					SCREAMING_SNAKE_CASE__						:	str									=       Dictionary.load(SCREAMING_SNAKE_CASE__						)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					SCREAMING_SNAKE_CASE__						:	int									=       target_dict.pad_index
																					SCREAMING_SNAKE_CASE__						:	Any									=       target_dict.bos_index
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       target_dict.pad_index
																					SCREAMING_SNAKE_CASE__						:	Optional[int]									=       target_dict.bos_index
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       target_dict.eos_index
																					SCREAMING_SNAKE_CASE__						:	List[str]									=       len(target_dict.symbols						)
																					SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       os.path.join(SCREAMING_SNAKE_CASE__       ,"""vocab.json"""						)
																					if not os.path.isdir(SCREAMING_SNAKE_CASE__						):
																												logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE__						)						)
																												return
																					os.makedirs(SCREAMING_SNAKE_CASE__       ,exist_ok=SCREAMING_SNAKE_CASE__						)
																					with open(SCREAMING_SNAKE_CASE__       ,"""w"""       ,encoding="""utf-8"""						) as vocab_handle:
																												json.dump(target_dict.indices       ,SCREAMING_SNAKE_CASE__						)
																					SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       WavaVecaCTCTokenizer(
																					    SCREAMING_SNAKE_CASE__       ,unk_token=target_dict.unk_word       ,pad_token=target_dict.pad_word       ,bos_token=target_dict.bos_word       ,eos_token=target_dict.eos_word       ,word_delimiter_token="""|"""       ,do_lower_case=SCREAMING_SNAKE_CASE__       ,)
																					SCREAMING_SNAKE_CASE__						:	Optional[int]									=       WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE__       ,tokenizer=SCREAMING_SNAKE_CASE__						)
																					processor.save_pretrained(SCREAMING_SNAKE_CASE__						)

														SCREAMING_SNAKE_CASE__						:	Any									=       SEWDForCTC(SCREAMING_SNAKE_CASE__						)
							else:
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       SEWDModel(SCREAMING_SNAKE_CASE__						)
														feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE__						)

							recursively_load_weights(SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__       ,SCREAMING_SNAKE_CASE__						)

							hf_model.save_pretrained(SCREAMING_SNAKE_CASE__						)


if __name__ == "__main__":
			UpperCAmelCase__				:   Optional[Any]   							=			argparse.ArgumentParser()
			parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
			parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
			parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
			parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
			parser.add_argument(
			    '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
			)
			UpperCAmelCase__				:   Tuple   							=			parser.parse_args()
			convert_sew_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
			)






"""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 lowercase_  (       _snake_case       ,_snake_case       ,_snake_case						):
							# Initialise PyTorch model
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       FNetConfig.from_json_file(_snake_case						)
							print(f'''Building PyTorch model from configuration: {config}'''						)
							SCREAMING_SNAKE_CASE__						:	List[str]									=       FNetForPreTraining(_snake_case						)

							SCREAMING_SNAKE_CASE__						:	Tuple									=       restore_checkpoint(_snake_case       ,_snake_case						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       checkpoint_dict["""target"""]

							# Embeddings
							# Position IDs
							SCREAMING_SNAKE_CASE__						:	Tuple									=       fnet_pretraining_model.state_dict()

							SCREAMING_SNAKE_CASE__						:	Any									=       state_dict["""fnet.embeddings.position_ids"""]
							SCREAMING_SNAKE_CASE__						:	List[str]									=       {"""fnet.embeddings.position_ids""": position_ids}
							# Embedding Layers
							SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""position"""]["""embedding"""][0]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""type"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Any									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""hidden_mapping_in"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	Dict									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""layer_norm"""]["""bias"""]						)

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

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""intermediate"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""kernel"""]						).T
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''feed_forward_{layer}''']["""output"""]["""bias"""]						)

														SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""scale"""]						)
														SCREAMING_SNAKE_CASE__						:	Tuple									=       torch.tensor(
														    pretrained_model_params["""encoder"""][f'''encoder_{layer}''']["""output_layer_norm"""]["""bias"""]						)

							# Pooler Layers
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""encoder"""]["""pooler"""]["""bias"""]						)

							# Masked LM Layers
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""kernel"""]						).T
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""predictions_dense"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	str									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""scale"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_layer_norm"""]["""bias"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""encoder"""]["""embedder"""]["""word"""]["""embedding"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""predictions_output"""]["""output_bias"""]						)
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.tensor(pretrained_model_params["""predictions_output"""]["""output_bias"""]						)

							# Seq Relationship Layers
							SCREAMING_SNAKE_CASE__						:	int									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_kernel"""]						)
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       torch.tensor(
							    pretrained_model_params["""classification"""]["""output_bias"""]						)

							# Load State Dict
							fnet_pretraining_model.load_state_dict(_snake_case						)

							# Save PreTrained
							print(f'''Saving pretrained model to {save_path}'''						)
							fnet_pretraining_model.save_pretrained(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__				:   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.')
					UpperCAmelCase__				:   Optional[Any]   							=			parser.parse_args()
					convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)




