
"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowercase				 =					logging.get_logger(__name__)

_lowercase				 =					'''▁'''

_lowercase				 =					{'''vocab_file''': '''sentencepiece.bpe.model'''}

_lowercase				 =					{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    }
}

_lowercase				 =					{
    '''xlm-roberta-base''': 5_12,
    '''xlm-roberta-large''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 5_12,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-english''': 5_12,
    '''xlm-roberta-large-finetuned-conll03-german''': 5_12,
}







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						_lowerCamelCase:     Optional[Any]   =     VOCAB_FILES_NAMES
						_lowerCamelCase:     List[str]   =     PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:     Union[str, Any]   =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:     Any   =     ['''input_ids''', '''attention_mask''']


						def __init__(			self  :   Union[str, Any]	,A_  :   str	,A_  :   str="<s>"	,A_  :   Any="</s>"	,A_  :   Tuple="</s>"	,A_  :   Any="<s>"	,A_  :   Optional[Any]="<unk>"	,A_  :   int="<pad>"	,A_  :   str="<mask>"	,A_  :   Optional[Dict[str, Any]] = None	,**A_  :   Optional[int]	,)  -> None:
							# Mask token behave like a normal word, i.e. include the space before it
							A     = AddedToken(A_	,lstrip=A_	,rstrip=A_   ) if isinstance(A_	,A_   ) else mask_token

							A     = {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_	,eos_token=A_	,unk_token=A_	,sep_token=A_	,cls_token=A_	,pad_token=A_	,mask_token=A_	,sp_model_kwargs=self.sp_model_kwargs	,**A_	,)

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.Load(str(A_   )   )
							A     = vocab_file

							# Original fairseq vocab and spm vocab must be "aligned":
							# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
							# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
							# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
							# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

							# Mimic fairseq token-to-id alignment for the first 4 token
							A     = {'<s>': 0, '<pad>': 1, '</s>': 2, '<unk>': 3}

							# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
							A     = 1

							A     = len(self.sp_model   ) + self.fairseq_offset
							A     = {v: k for k, v in self.fairseq_tokens_to_ids.items()}


						def __getstate__(			self  :   Union[str, Any]   )  -> Any:
							A     = self.__dict__.copy()
							A     = None
							A     = self.sp_model.serialized_model_proto()
							return state


						def __setstate__(			self  :   str	,A_  :   str   )  -> Optional[Any]:
							A     = d

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

							A     = spm.SentencePieceProcessor(**self.sp_model_kwargs   )
							self.sp_model.LoadFromSerializedProto(self.sp_model_proto   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:

							if token_ids_a is None:
								return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							A     = [self.cls_token_id]
							A     = [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]	,A_  :   List[int]	,A_  :   Optional[List[int]] = None	,A_  :   bool = False   )  -> List[int]:

							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_	,token_ids_a=A_	,already_has_special_tokens=A_   )

							if token_ids_a is None:
								return [1] + ([0] * len(A_   )) + [1]
							return [1] + ([0] * len(A_   )) + [1, 1] + ([0] * len(A_   )) + [1]


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   List[int]	,A_  :   Optional[List[int]] = None   )  -> List[int]:
							A     = [self.sep_token_id]
							A     = [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]


						@property
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> Optional[Any]:
							return len(self.sp_model   ) + self.fairseq_offset + 1  # Add the <mask> token


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Optional[Any]:
							A     = {self.convert_ids_to_tokens(A_   ): i for i in range(self.vocab_size   )}
							vocab.update(self.added_tokens_encoder   )
							return vocab


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> List[str]:
							return self.sp_model.encode(A_	,out_type=A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Tuple	,A_  :   List[Any]   )  -> Tuple:
							if token in self.fairseq_tokens_to_ids:
								return self.fairseq_tokens_to_ids[token]
							A     = self.sp_model.PieceToId(A_   )

							# Need to return unknown token if the SP model returned 0
							return spm_id + self.fairseq_offset if spm_id else self.unk_token_id


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   str   )  -> int:
							if index in self.fairseq_ids_to_tokens:
								return self.fairseq_ids_to_tokens[index]
							return self.sp_model.IdToPiece(index - self.fairseq_offset   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,A_  :   Optional[Any]   )  -> List[Any]:
							A     = ''.join(A_   ).replace(A_	,' '   ).strip()
							return out_string




						def     _SCREAMING_SNAKE_CASE   (			self  :   Union[str, Any]	,A_  :   str	,A_  :   Optional[str] = None   )  -> Tuple[str]:
							if not os.path.isdir(A_   ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'   )
								return
							A     = os.path.join(
							    A_	,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']   )

							if os.path.abspath(self.vocab_file   ) != os.path.abspath(A_   ) and os.path.isfile(self.vocab_file   ):
								copyfile(self.vocab_file	,A_   )
							elif not os.path.isfile(self.vocab_file   ):
								with open(A_	,'wb'   ) as fi:
									A     = self.sp_model.serialized_model_proto()
									fi.write(A_   )

							return (out_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
					)


