






import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			'''▁'''

SCREAMING_SNAKE_CASE__				     =			{'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

SCREAMING_SNAKE_CASE__				     =			{
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

SCREAMING_SNAKE_CASE__				     =			{
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

SCREAMING_SNAKE_CASE__				     =			{
    '''ernie-m-base''': 514,
    '''ernie-m-large''': 514,
}

SCREAMING_SNAKE_CASE__				     =			{
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       List[str]							=							["input_ids"]

		__SCREAMING_SNAKE_CASE  :       Optional[Any]							=							VOCAB_FILES_NAMES
		__SCREAMING_SNAKE_CASE  :       Optional[Any]							=							PRETRAINED_INIT_CONFIGURATION
		__SCREAMING_SNAKE_CASE  :       Dict							=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		__SCREAMING_SNAKE_CASE  :       List[Any]							=							PRETRAINED_VOCAB_FILES_MAP
		__SCREAMING_SNAKE_CASE  :       int							=							RESOURCE_FILES_NAMES

		def __init__(					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Any=None    ,       SCREAMING_SNAKE_CASE__		:    List[str]=False    ,       SCREAMING_SNAKE_CASE__		:    List[str]="utf8"    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]="[UNK]"    ,       SCREAMING_SNAKE_CASE__		:    List[str]="[SEP]"    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]="[PAD]"    ,       SCREAMING_SNAKE_CASE__		:    List[str]="[CLS]"    ,       SCREAMING_SNAKE_CASE__		:    Tuple="[MASK]"    ,       SCREAMING_SNAKE_CASE__		:    Optional[Dict[str, Any]] = None    ,       **SCREAMING_SNAKE_CASE__		:    List[Any]    ,       ):



					'''simple docstring'''



					__a						:  Tuple									=     {} if sp_model_kwargs is None else sp_model_kwargs
					super().__init__(
					    do_lower_case=SCREAMING_SNAKE_CASE__    ,       unk_token=SCREAMING_SNAKE_CASE__    ,       sep_token=SCREAMING_SNAKE_CASE__    ,       pad_token=SCREAMING_SNAKE_CASE__    ,       cls_token=SCREAMING_SNAKE_CASE__    ,       mask_token=SCREAMING_SNAKE_CASE__    ,       vocab_file=SCREAMING_SNAKE_CASE__    ,       encoding=SCREAMING_SNAKE_CASE__    ,       sp_model_kwargs=self.sp_model_kwargs    ,       **SCREAMING_SNAKE_CASE__    ,       )
					__a						:  Optional[int]									=     do_lower_case
					__a						:  List[str]									=     sentencepiece_model_ckpt
					__a						:  Optional[Any]									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(SCREAMING_SNAKE_CASE__    )

					# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
					if vocab_file is not None:
								__a						:  Optional[Any]									=     self.load_vocab(filepath=SCREAMING_SNAKE_CASE__    )
					else:
								__a						:  int									=     {self.sp_model.id_to_piece(SCREAMING_SNAKE_CASE__    ): id for id in range(self.sp_model.get_piece_size()    )}
					__a						:  str									=     {v: k for k, v in self.vocab.items()}

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ):



					'''simple docstring'''



					if text is None:
								return None

					__a						:  List[Any]									=     self.tokenize(SCREAMING_SNAKE_CASE__    )
					__a						,					__a						:  Union[str, Any]									=     '', []

					for i, ch in enumerate(SCREAMING_SNAKE_CASE__    ):
								if ch in self.SP_CHAR_MAPPING:
											__a						:  List[str]									=     self.SP_CHAR_MAPPING.get(SCREAMING_SNAKE_CASE__    )
								else:
											__a						:  Any									=     unicodedata.normalize('NFKC'    ,       SCREAMING_SNAKE_CASE__    )
								if self.is_whitespace(SCREAMING_SNAKE_CASE__    ):
											continue
								normalized_text += ch
								char_mapping.extend([i] * len(SCREAMING_SNAKE_CASE__    )    )

					__a						,					__a						,					__a						:  str									=     normalized_text, [], 0

					if self.do_lower_case:
								__a						:  Tuple									=     text.lower()

					for token in split_tokens:
								if token[:1] == "▁":
											__a						:  List[Any]									=     token[1:]
								__a						:  Optional[Any]									=     text[offset:].index(SCREAMING_SNAKE_CASE__    ) + offset
								__a						:  List[Any]									=     start + len(SCREAMING_SNAKE_CASE__    )

								token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)    )
								__a						:  Tuple									=     end
					return token_mapping

		@property
		def    __lowerCAmelCase  (					self		:    Dict    ):



					'''simple docstring'''



					return len(self.vocab    )

		def    __lowerCAmelCase  (					self		:    List[Any]    ):



					'''simple docstring'''



					return dict(self.vocab    ,       **self.added_tokens_encoder    )

		def __getstate__(					self		:    Tuple    ):



					'''simple docstring'''



					__a						:  Tuple									=     self.__dict__.copy()
					__a						:  Any									=     None
					return state

		def __setstate__(					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ):



					'''simple docstring'''



					__a						:  Optional[Any]									=     d

					# for backward compatibility
					if not hasattr(self    ,       'sp_model_kwargs'    ):
								__a						:  str									=     {}

					__a						:  Optional[int]									=     spm.SentencePieceProcessor(**self.sp_model_kwargs    )
					self.sp_model.Load(self.sentencepiece_model_ckpt    )

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					return "".join((self.SP_CHAR_MAPPING.get(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ) for c in text)    )

		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    List[str]    ,       SCREAMING_SNAKE_CASE__		:    int=False    ,       SCREAMING_SNAKE_CASE__		:    Tuple=6_4    ,       SCREAMING_SNAKE_CASE__		:    List[str]=0.1    ):



					'''simple docstring'''



					if self.sp_model_kwargs.get('enable_sampling'    ) is True:
								__a						:  Dict									=     True
					if self.sp_model_kwargs.get('alpha'    ) is not None:
								__a						:  int									=     self.sp_model_kwargs.get('alpha'    )
					if self.sp_model_kwargs.get('nbest_size'    ) is not None:
								__a						:  Any									=     self.sp_model_kwargs.get('nbest_size'    )

					if not enable_sampling:
								__a						:  int									=     self.sp_model.EncodeAsPieces(SCREAMING_SNAKE_CASE__    )
					else:
								__a						:  Optional[Any]									=     self.sp_model.SampleEncodeAsPieces(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    )
					__a						:  str									=     []
					for pi, piece in enumerate(SCREAMING_SNAKE_CASE__    ):
								if piece == SPIECE_UNDERLINE:
											if not pieces[pi + 1].startswith(SCREAMING_SNAKE_CASE__    ) and pi != 0:
														new_pieces.append(SCREAMING_SNAKE_CASE__    )
														continue
											else:
														continue
								__a						:  Optional[int]									=     0
								for i, chunk in enumerate(SCREAMING_SNAKE_CASE__    ):
											if chunk == SPIECE_UNDERLINE:
														continue
											if self.is_ch_char(SCREAMING_SNAKE_CASE__    ) or self.is_punct(SCREAMING_SNAKE_CASE__    ):
														if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																	new_pieces.append(piece[lst_i:i]    )
														new_pieces.append(SCREAMING_SNAKE_CASE__    )
														__a						:  Union[str, Any]									=     i + 1
											elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
														if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																	new_pieces.append(piece[lst_i:i]    )
														__a						:  int									=     i
											elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
														if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
																	new_pieces.append(piece[lst_i:i]    )
														__a						:  Dict									=     i
								if len(SCREAMING_SNAKE_CASE__    ) > lst_i:
											new_pieces.append(piece[lst_i:]    )
					return new_pieces

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ):



					'''simple docstring'''



					__a						:  int									=     ''.join(SCREAMING_SNAKE_CASE__    ).replace(SCREAMING_SNAKE_CASE__    ,       ' '    ).strip()
					return out_string

		def    __lowerCAmelCase  (					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					__a						:  str									=     self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[Any]									=     ''.join(SCREAMING_SNAKE_CASE__    ).replace(SCREAMING_SNAKE_CASE__    ,       ' '    ).strip()
					return out_string

		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]    ):



					'''simple docstring'''



					return self.vocab.get(SCREAMING_SNAKE_CASE__    ,       self.vocab.get(self.unk_token    )    )

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					return self.reverse_vocab.get(SCREAMING_SNAKE_CASE__    ,       self.unk_token    )

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=None    ):



					'''simple docstring'''



					if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					__a						:  Union[str, Any]									=     [self.cls_token_id]
					__a						:  Union[str, Any]									=     [self.sep_token_id]
					return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep

		def    __lowerCAmelCase  (					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    str=None    ):



					'''simple docstring'''



					if offset_mapping_a is None:
								return [(0, 0)] + offset_mapping_a + [(0, 0)]

					return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]

		def    __lowerCAmelCase  (					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=None    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=False    ):



					'''simple docstring'''



					if already_has_special_tokens:
								if token_ids_a is not None:
											raise ValueError(
											    'You should not supply a second sequence if the provided sequence of '
											    'ids is already formatted with special tokens for the model.'    )
								return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

					if token_ids_a is not None:
								return [1] + ([0] * len(SCREAMING_SNAKE_CASE__    )) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__    )) + [1]
					return [1] + ([0] * len(SCREAMING_SNAKE_CASE__    )) + [1]

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    List[int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[List[int]] = None    ):



					'''simple docstring'''



					if token_ids_a is None:
								# [CLS] X [SEP]
								return (len(SCREAMING_SNAKE_CASE__    ) + 2) * [0]

					# [CLS] A [SEP] [SEP] B [SEP]
					return [0] * (len(SCREAMING_SNAKE_CASE__    ) + 1) + [1] * (len(SCREAMING_SNAKE_CASE__    ) + 3)

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					if "\u4e00" <= char <= "\u9fff":
								return True
					return False

		def    __lowerCAmelCase  (					self		:    Union[str, Any]    ,       SCREAMING_SNAKE_CASE__		:    str    ):



					'''simple docstring'''



					if ("a" <= char <= "z") or ("A" <= char <= "Z"):
								return True
					return False

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Any    ):



					'''simple docstring'''



					if char in ",;:.?!~，；：。？！《》【】":
								return True
					return False

		def    __lowerCAmelCase  (					self		:    str    ,       SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					if char == " " or char == "\t" or char == "\n" or char == "\r":
								return True
					if len(SCREAMING_SNAKE_CASE__    ) == 1:
								__a						:  str									=     unicodedata.category(SCREAMING_SNAKE_CASE__    )
								if cat == "Zs":
											return True
					return False

		def    __lowerCAmelCase  (					self		:    List[Any]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]    ):



					'''simple docstring'''



					__a						:  Union[str, Any]									=     {}
					with io.open(SCREAMING_SNAKE_CASE__    ,       'r'    ,       encoding='utf-8'    ) as f:
								for index, line in enumerate(SCREAMING_SNAKE_CASE__    ):
											__a						:  Optional[Any]									=     line.rstrip('\n'    )
											__a						:  Dict									=     int(SCREAMING_SNAKE_CASE__    )

					return token_to_idx



		def    __lowerCAmelCase  (					self		:    Any    ,       SCREAMING_SNAKE_CASE__		:    str    ,       SCREAMING_SNAKE_CASE__		:    Optional[str] = None    ):



					'''simple docstring'''



					__a						:  List[Any]									=     0
					if os.path.isdir(SCREAMING_SNAKE_CASE__    ):
								__a						:  Union[str, Any]									=     os.path.join(
								    SCREAMING_SNAKE_CASE__    ,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']    )
					else:
								__a						:  Union[str, Any]									=     (filename_prefix + '-' if filename_prefix else '') + save_directory
					with open(SCREAMING_SNAKE_CASE__    ,       'w'    ,       encoding='utf-8'    ) as writer:
								for token, token_index in sorted(self.vocab.items()    ,       key=lambda SCREAMING_SNAKE_CASE__    : kv[1]    ):
											if index != token_index:
														logger.warning(
														    f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
														    ' Please check that the vocabulary is not corrupted!'    )
														__a						:  List[Any]									=     token_index
											writer.write(token + '\n'    )
											index += 1

					__a						:  List[Any]									=     os.path.join(SCREAMING_SNAKE_CASE__    ,       'sentencepiece.bpe.model'    )
					with open(SCREAMING_SNAKE_CASE__    ,       'wb'    ) as fi:
								__a						:  str									=     self.sp_model.serialized_model_proto()
								fi.write(SCREAMING_SNAKE_CASE__    )

					return (vocab_file,)









'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase   : int             =				logging.get_logger(__name__)

lowerCAmelCase   : Dict             =				{
    """post_extract_proj""": """feature_projection.projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """self_attn.linear_k""": """encoder.layers.*.self_attn.linear_k""",
    """self_attn.linear_v""": """encoder.layers.*.self_attn.linear_v""",
    """self_attn.linear_q""": """encoder.layers.*.self_attn.linear_q""",
    """self_attn.pos_bias_u""": """encoder.layers.*.self_attn.pos_bias_u""",
    """self_attn.pos_bias_v""": """encoder.layers.*.self_attn.pos_bias_v""",
    """self_attn.linear_out""": """encoder.layers.*.self_attn.linear_out""",
    """self_attn.linear_pos""": """encoder.layers.*.self_attn.linear_pos""",
    """self_attn.rotary_emb""": """encoder.embed_positions""",
    """self_attn_layer_norm""": """encoder.layers.*.self_attn_layer_norm""",
    """conv_module.pointwise_conv1""": """encoder.layers.*.conv_module.pointwise_conv1""",
    """conv_module.pointwise_conv2""": """encoder.layers.*.conv_module.pointwise_conv2""",
    """conv_module.depthwise_conv""": """encoder.layers.*.conv_module.depthwise_conv""",
    """conv_module.batch_norm""": """encoder.layers.*.conv_module.batch_norm""",
    """conv_module.layer_norm""": """encoder.layers.*.conv_module.layer_norm""",
    """ffn1.w_1""": """encoder.layers.*.ffn1.intermediate_dense""",
    """ffn1.w_2""": """encoder.layers.*.ffn1.output_dense""",
    """ffn1.layer_norm""": """encoder.layers.*.ffn1_layer_norm""",
    """ffn2.w_1""": """encoder.layers.*.ffn2.intermediate_dense""",
    """ffn2.w_2""": """encoder.layers.*.ffn2.output_dense""",
    """ffn2.layer_norm""": """encoder.layers.*.ffn2_layer_norm""",
    """final_layer_norm""": """encoder.layers.*.final_layer_norm""",
    """encoder.layer_norm""": """encoder.layer_norm""",
    """w2v_model.layer_norm""": """feature_projection.layer_norm""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCAmelCase   : Optional[Any]             =				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]




def 	_A  (       A							,A							,A							,A							,A							)  ->				Tuple:
	for attribute in key.split("."							):
		lowercase						:		Dict   					=		getattr(A							,A							)

	if weight_type is not None:
		lowercase						:		List[str]   					=		getattr(A							,A							).shape
	else:
		lowercase						:		Any   					=		hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    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":
		lowercase						:		List[Any]   					=		value
	elif weight_type == "weight_g":
		lowercase						:		List[Any]   					=		value
	elif weight_type == "weight_v":
		lowercase						:		int   					=		value
	elif weight_type == "bias":
		lowercase						:		Any   					=		value
	elif weight_type == "running_mean":
		lowercase						:		Tuple   					=		value
	elif weight_type == "running_var":
		lowercase						:		Dict   					=		value
	elif weight_type == "num_batches_tracked":
		lowercase						:		Optional[int]   					=		value
	elif weight_type == "inv_freq":
		lowercase						:		List[Any]   					=		value
	else:
		lowercase						:		Dict   					=		value

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




def 	_A  (       A							,A							,A							)  ->				int:
	lowercase						:		Optional[int]   					=		[]
	lowercase						:		Tuple   					=		fairseq_model.state_dict()

	lowercase						:		Optional[Any]   					=		hf_model.wavaveca_conformer.feature_extractor

	for name, value in fairseq_dict.items():
		lowercase						:		str   					=		False
		if "conv_layers" in name:
			load_conv_layer(
			    A							,A							,A							,A							,hf_model.config.feat_extract_norm == "group"							,)
			lowercase						:		Dict   					=		True
		else:
			for key, mapped_key in MAPPING.items():
				lowercase						:		List[str]   					=		"wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split("w2v_model."							)[-1] == name.split("."							)[0]:
					lowercase						:		List[str]   					=		True
					if "*" in mapped_key:
						lowercase						:		int   					=		name.split(A							)[0].split("."							)[-2]
						lowercase						:		Optional[Any]   					=		mapped_key.replace("*"							,A							)
					if "pos_bias_u" in name:
						lowercase						:		str   					=		None
					elif "pos_bias_v" in name:
						lowercase						:		Optional[int]   					=		None
					elif "weight_g" in name:
						lowercase						:		int   					=		"weight_g"
					elif "weight_v" in name:
						lowercase						:		Dict   					=		"weight_v"
					elif "bias" in name:
						lowercase						:		List[str]   					=		"bias"
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						lowercase						:		Optional[int]   					=		"weight"
					elif "running_mean" in name:
						lowercase						:		Optional[int]   					=		"running_mean"
					elif "inv_freq" in name:
						lowercase						:		Dict   					=		"inv_freq"
					elif "running_var" in name:
						lowercase						:		int   					=		"running_var"
					elif "num_batches_tracked" in name:
						lowercase						:		Optional[Any]   					=		"num_batches_tracked"
					else:
						lowercase						:		int   					=		None
					set_recursively(A							,A							,A							,A							,A							)
				continue
		if not is_used:
			unused_weights.append(A							)

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




def 	_A  (       A							,A							,A							,A							,A							)  ->				List[str]:
	lowercase						:		Tuple   					=		full_name.split("conv_layers."							)[-1]
	lowercase						:		Optional[Any]   					=		name.split("."							)
	lowercase						:		str   					=		int(items[0]							)
	lowercase						:		List[Any]   					=		int(items[1]							)

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''							)
			lowercase						:		Union[str, Any]   					=		value
			logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''							)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''							)
			lowercase						:		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:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''							)
			lowercase						:		int   					=		value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    F'''{full_name} has size {value.shape}, but'''
				    F''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''							)
			lowercase						:		str   					=		value
			logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''							)
	else:
		unused_weights.append(A							)




@torch.no_grad()
def 	_A  (       A							,A							,A=None							,A=None							,A=True							)  ->				Optional[Any]:
	if config_path is not None:
		lowercase						:		Tuple   					=		WavaVecaConformerConfig.from_pretrained(A							,hidden_act="swish"							)
	else:
		lowercase						:		Any   					=		WavaVecaConformerConfig()

	if "rope" in checkpoint_path:
		lowercase						:		str   					=		"rotary"

	if is_finetuned:
		if dict_path:
			lowercase						:		List[str]   					=		Dictionary.load(A							)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			lowercase						:		Optional[int]   					=		target_dict.pad_index
			lowercase						:		Optional[int]   					=		target_dict.bos_index
			lowercase						:		Optional[Any]   					=		target_dict.eos_index
			lowercase						:		str   					=		len(target_dict.symbols							)
			lowercase						:		List[Any]   					=		os.path.join(A							,"vocab.json"							)
			if not os.path.isdir(A							):
				logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(A							)							)
				return
			os.makedirs(A							,exist_ok=A							)
			lowercase						:		Any   					=		target_dict.indices

			# fairseq has the <pad> and <s> switched
			lowercase						:		Any   					=		0
			lowercase						:		Tuple   					=		1
			with open(A							,"w"							,encoding="utf-8"							) as vocab_handle:
				json.dump(A							,A							)
			lowercase						:		Tuple   					=		WavaVecaCTCTokenizer(
			    A							,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=A							,)
			lowercase						:		Dict   					=		True if config.feat_extract_norm == "layer" else False
			lowercase						:		List[str]   					=		WavaVecaFeatureExtractor(
			    feature_size=1							,sampling_rate=1_6_0_0_0							,padding_value=0							,do_normalize=A							,return_attention_mask=A							,)
			lowercase						:		List[str]   					=		WavaVecaProcessor(feature_extractor=A							,tokenizer=A							)
			processor.save_pretrained(A							)

		lowercase						:		Any   					=		WavaVecaConformerForCTC(A							)
	else:
		lowercase						:		str   					=		WavaVecaConformerForPreTraining(A							)

	if is_finetuned:
		lowercase					,		lowercase					,		lowercase						:		List[str]   					=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]							,arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
	else:
		lowercase						:		List[str]   					=		argparse.Namespace(task="audio_pretraining"							)
		lowercase						:		Union[str, Any]   					=		fairseq.tasks.setup_task(A							)

		lowercase					,		lowercase					,		lowercase						:		Optional[Any]   					=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,task=A							)

	lowercase						:		List[str]   					=		model[0].eval()

	recursively_load_weights(A							,A							,not is_finetuned							)

	hf_wavavec.save_pretrained(A							)


if __name__ == "__main__":
					lowerCAmelCase   : 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(
					    """--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
					)
					lowerCAmelCase   : int             =				parser.parse_args()
					convert_wavaveca_conformer_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
					)


