


"""simple docstring"""






import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


lowercase  		=			logging.get_logger(__name__)

lowercase  		=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

lowercase  		=			{
    """vocab_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"""
        ),
    },
    """merges_file""": {
        """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt""",
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"""
        ),
    },
    """tokenizer_file""": {
        """allenai/longformer-base-4096""": (
            """https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096""": (
            """https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-finetuned-triviaqa""": (
            """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-base-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
        """allenai/longformer-large-4096-extra.pos.embd.only""": (
            """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"""
        ),
    },
}

lowercase  		=			{
    """allenai/longformer-base-4096""": 4096,
    """allenai/longformer-large-4096""": 4096,
    """allenai/longformer-large-4096-finetuned-triviaqa""": 4096,
    """allenai/longformer-base-4096-extra.pos.embd.only""": 4096,
    """allenai/longformer-large-4096-extra.pos.embd.only""": 4096,
}


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Optional[int]				  =  VOCAB_FILES_NAMES
							__magic_name__					:     Union[str, Any]				  =  PRETRAINED_VOCAB_FILES_MAP
							__magic_name__					:     List[Any]				  =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							__magic_name__					:     List[Any]				  =  ['''input_ids''', '''attention_mask''']
							__magic_name__					:     Dict				  =  LongformerTokenizer




							def __init__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="replace"	,					lowerCamelCase__="<s>"	,					lowerCamelCase__="</s>"	,					lowerCamelCase__="</s>"	,					lowerCamelCase__="<s>"	,					lowerCamelCase__="<unk>"	,					lowerCamelCase__="<pad>"	,					lowerCamelCase__="<mask>"	,					lowerCamelCase__=False	,					lowerCamelCase__=True	,					**lowerCamelCase__	,					)   ->	List[str]:







								'''simple docstring'''




								super().__init__(
								    lowerCamelCase__	,					lowerCamelCase__	,					tokenizer_file=lowerCamelCase__	,					errors=lowerCamelCase__	,					bos_token=lowerCamelCase__	,					eos_token=lowerCamelCase__	,					sep_token=lowerCamelCase__	,					cls_token=lowerCamelCase__	,					unk_token=lowerCamelCase__	,					pad_token=lowerCamelCase__	,					mask_token=lowerCamelCase__	,					add_prefix_space=lowerCamelCase__	,					trim_offsets=lowerCamelCase__	,					**lowerCamelCase__	,					)

								snake_case__  :						Tuple		=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
								if pre_tok_state.get("add_prefix_space"	,					lowerCamelCase__) != add_prefix_space:
									snake_case__  :						Optional[Any]		=      getattr(lowerCamelCase__	,					pre_tok_state.pop("type"))
									snake_case__  :						Tuple		=      add_prefix_space
									snake_case__  :						Any		=      pre_tok_class(**lowerCamelCase__)

								snake_case__  :						Tuple		=      add_prefix_space

								snake_case__  :						Any		=      "post_processor"
								snake_case__  :						List[Any]		=      getattr(self.backend_tokenizer	,					lowerCamelCase__	,					lowerCamelCase__)
								if tokenizer_component_instance:
									snake_case__  :						List[Any]		=      json.loads(tokenizer_component_instance.__getstate__())

									# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
									if "sep" in state:
										snake_case__  :						int		=      tuple(state["sep"])
									if "cls" in state:
										snake_case__  :						str		=      tuple(state["cls"])

									snake_case__  :						Union[str, Any]		=      False

									if state.get("add_prefix_space"	,					lowerCamelCase__) != add_prefix_space:
										snake_case__  :						List[str]		=      add_prefix_space
										snake_case__  :						str		=      True

									if state.get("trim_offsets"	,					lowerCamelCase__) != trim_offsets:
										snake_case__  :						Union[str, Any]		=      trim_offsets
										snake_case__  :						int		=      True

									if changes_to_apply:
										snake_case__  :						Union[str, Any]		=      getattr(lowerCamelCase__	,					state.pop("type"))
										snake_case__  :						List[str]		=      component_class(**lowerCamelCase__)
										setattr(self.backend_tokenizer	,					lowerCamelCase__	,					lowerCamelCase__)




							@property
							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								if self._mask_token is None:
									if self.verbose:
										logger.error("Using mask_token, but it is not set yet.")
									return None
								return str(self._mask_token)




							@mask_token.setter
							def        UpperCAmelCase (						self	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      AddedToken(lowerCamelCase__	,					lstrip=lowerCamelCase__	,					rstrip=lowerCamelCase__) if isinstance(lowerCamelCase__	,					lowerCamelCase__) else value
								snake_case__  :						Tuple		=      value




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	BatchEncoding:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      kwargs.get("is_split_into_words"	,					lowerCamelCase__)
								assert self.add_prefix_space or not is_split_into_words, (
								    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
								    "to use it with pretokenized inputs."
								)

								return super()._batch_encode_plus(*lowerCamelCase__	,					**lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	BatchEncoding:







								'''simple docstring'''




								snake_case__  :						str		=      kwargs.get("is_split_into_words"	,					lowerCamelCase__)

								assert self.add_prefix_space or not is_split_into_words, (
								    f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
								    "to use it with pretokenized inputs."
								)

								return super()._encode_plus(*lowerCamelCase__	,					**lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__ = None)   ->	Tuple[str]:







								'''simple docstring'''




								snake_case__  :						str		=      self._tokenizer.model.save(lowerCamelCase__	,					name=lowerCamelCase__)
								return tuple(lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__=None)   ->	str:







								'''simple docstring'''




								snake_case__  :						Tuple		=      [self.bos_token_id] + token_ids_a + [self.eos_token_id]
								if token_ids_a is None:
									return output

								return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__ = None)   ->	List[int]:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      [self.sep_token_id]
								snake_case__  :						List[str]		=      [self.cls_token_id]

								if token_ids_a is None:
									return len(cls + token_ids_a + sep) * [0]
								return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]











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()
lowercase_      :							Dict    							=					logging.get_logger(__name__)

lowercase_      :							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 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						for attribute in key.split("."		):
												_UpperCAmelCase						=	getattr(snake_case_      ,       snake_case_		)

						if weight_type is not None:
												_UpperCAmelCase						=	getattr(snake_case_      ,       snake_case_		).shape
						else:
												_UpperCAmelCase						=	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":
												_UpperCAmelCase						=	value
						elif weight_type == "weight_g":
												_UpperCAmelCase						=	value
						elif weight_type == "weight_v":
												_UpperCAmelCase						=	value
						elif weight_type == "bias":
												_UpperCAmelCase						=	value
						else:
												_UpperCAmelCase						=	value

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	[]
						_UpperCAmelCase						=	fairseq_model.state_dict()

						_UpperCAmelCase						=	hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												_UpperCAmelCase						=	False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       hf_model.config.feat_extract_norm == "group"      ,       )
																		_UpperCAmelCase						=	True
												else:
																		for key, mapped_key in MAPPING.items():
																								_UpperCAmelCase						=	"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]:
																														_UpperCAmelCase						=	True
																														if "*" in mapped_key:
																																				_UpperCAmelCase						=	name.split(snake_case_		)[0].split("."		)[-2]
																																				if not layer_index.isnumeric():
																																										continue
																																				_UpperCAmelCase						=	mapped_key.replace("*"      ,       snake_case_		)
																														if "weight_g" in name:
																																				_UpperCAmelCase						=	"weight_g"
																														elif "weight_v" in name:
																																				_UpperCAmelCase						=	"weight_v"
																														elif "weight" in name:
																																				_UpperCAmelCase						=	"weight"
																														elif "bias" in name:
																																				_UpperCAmelCase						=	"bias"
																														else:
																																				_UpperCAmelCase						=	None
																														set_recursively(snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		)
																								continue
												if not is_used:
																		unused_weights.append(snake_case_		)

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	full_name.split("conv_layers."		)[-1]
						_UpperCAmelCase						=	name.split("."		)
						_UpperCAmelCase						=	int(items[0]		)
						_UpperCAmelCase						=	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."""
																		)
																		_UpperCAmelCase						=	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."""
																		)
																		_UpperCAmelCase						=	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."
																		)
																		_UpperCAmelCase						=	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."""
																		)
																		_UpperCAmelCase						=	value
																		logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""		)
						else:
												unused_weights.append(snake_case_		)


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_UpperCAmelCase						=	SEWDConfig()
						if is_finetuned:
												_UpperCAmelCase						=	model.wav_encoder.wav_model.cfg
						else:
												_UpperCAmelCase						=	model.cfg

						_UpperCAmelCase						=	fs_config.conv_bias
						_UpperCAmelCase						=	eval(fs_config.conv_feature_layers		)
						_UpperCAmelCase						=	[x[0] for x in conv_layers]
						_UpperCAmelCase						=	[x[1] for x in conv_layers]
						_UpperCAmelCase						=	[x[2] for x in conv_layers]
						_UpperCAmelCase						=	"gelu"
						_UpperCAmelCase						=	"layer" if fs_config.extractor_mode == "layer_norm" else "group"
						_UpperCAmelCase						=	0.0
						_UpperCAmelCase						=	fs_config.activation_fn.name
						_UpperCAmelCase						=	fs_config.encoder_embed_dim
						_UpperCAmelCase						=	0.02
						_UpperCAmelCase						=	fs_config.encoder_ffn_embed_dim
						_UpperCAmelCase						=	1e-5
						_UpperCAmelCase						=	fs_config.encoder_layerdrop
						_UpperCAmelCase						=	fs_config.encoder_attention_heads
						_UpperCAmelCase						=	fs_config.conv_pos_groups
						_UpperCAmelCase						=	fs_config.conv_pos
						_UpperCAmelCase						=	len(snake_case_		)
						_UpperCAmelCase						=	fs_config.encoder_layers
						_UpperCAmelCase						=	fs_config.squeeze_factor
						# DeBERTa-specific parameters:
						_UpperCAmelCase						=	fs_config.max_position_embeddings
						_UpperCAmelCase						=	fs_config.position_buckets
						_UpperCAmelCase						=	fs_config.share_att_key
						_UpperCAmelCase						=	fs_config.relative_attention
						_UpperCAmelCase						=	fs_config.position_biased_input
						_UpperCAmelCase						=	tuple(fs_config.pos_att_type.split("|"		)		)
						_UpperCAmelCase						=	fs_config.norm_rel_ebd

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												_UpperCAmelCase						=	model.cfg
												_UpperCAmelCase						=	fs_config.final_dropout
												_UpperCAmelCase						=	fs_config.layerdrop
						_UpperCAmelCase						=	fs_config.activation_dropout
						_UpperCAmelCase						=	fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						_UpperCAmelCase						=	fs_config.attention_dropout
						_UpperCAmelCase						=	fs_config.dropout_input
						_UpperCAmelCase						=	fs_config.dropout
						_UpperCAmelCase						=	fs_config.mask_channel_length
						_UpperCAmelCase						=	fs_config.mask_channel_prob
						_UpperCAmelCase						=	fs_config.mask_length
						_UpperCAmelCase						=	fs_config.mask_prob

						_UpperCAmelCase						=	"Wav2Vec2FeatureExtractor"
						_UpperCAmelCase						=	"Wav2Vec2CTCTokenizer"

						return config


@torch.no_grad()
def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_=None      ,       snake_case_=None      ,       snake_case_=True		):

						'''simple docstring'''


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

						if config_path is not None:
												_UpperCAmelCase						=	SEWDConfig.from_pretrained(snake_case_		)
						else:
												_UpperCAmelCase						=	convert_config(model[0]      ,       snake_case_		)
						_UpperCAmelCase						=	model[0].eval()

						_UpperCAmelCase						=	True if config.feat_extract_norm == "layer" else False
						_UpperCAmelCase						=	WavaVecaFeatureExtractor(
						    feature_size=1      ,       sampling_rate=1_6000      ,       padding_value=0      ,       do_normalize=snake_case_      ,       return_attention_mask=snake_case_      ,       )

						if is_finetuned:
												if dict_path:
																		_UpperCAmelCase						=	Dictionary.load(snake_case_		)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		_UpperCAmelCase						=	target_dict.pad_index
																		_UpperCAmelCase						=	target_dict.bos_index
																		_UpperCAmelCase						=	target_dict.pad_index
																		_UpperCAmelCase						=	target_dict.bos_index
																		_UpperCAmelCase						=	target_dict.eos_index
																		_UpperCAmelCase						=	len(target_dict.symbols		)
																		_UpperCAmelCase						=	os.path.join(snake_case_      ,       "vocab.json"		)
																		if not os.path.isdir(snake_case_		):
																								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(snake_case_		)		)
																								return
																		os.makedirs(snake_case_      ,       exist_ok=snake_case_		)
																		with open(snake_case_      ,       "w"      ,       encoding="utf-8"		) as vocab_handle:
																								json.dump(target_dict.indices      ,       snake_case_		)
																		_UpperCAmelCase						=	WavaVecaCTCTokenizer(
																		    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=snake_case_      ,       )
																		_UpperCAmelCase						=	WavaVecaProcessor(feature_extractor=snake_case_      ,       tokenizer=snake_case_		)
																		processor.save_pretrained(snake_case_		)

												_UpperCAmelCase						=	SEWDForCTC(snake_case_		)
						else:
												_UpperCAmelCase						=	SEWDModel(snake_case_		)
												feature_extractor.save_pretrained(snake_case_		)

						recursively_load_weights(snake_case_      ,       snake_case_      ,       snake_case_		)

						hf_model.save_pretrained(snake_case_		)


if __name__ == "__main__":
				lowercase_      :							str    							=					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'
				)
				lowercase_      :							Tuple    							=					parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)




