"""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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_a					:					str=			logging.get_logger(__name__)

_a					:					List[Any]=			{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.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   __UpperCAmelCase    (   UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    List[Any]      ,							UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    Tuple      ,							UpperCAmelCase_   :    Any  )							->      Union[str, Any]:

	'''simple docstring'''





	for attribute in key.split('.'  ):
		__snake_case      :							List[Any]						=			getattr(UpperCAmelCase_      ,							UpperCAmelCase_  )

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

	logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    Optional[Any]      ,							UpperCAmelCase_   :    Union[str, Any]  )							->      Optional[int]:

	'''simple docstring'''





	__snake_case      :							Tuple						=			[]
	__snake_case      :							Any						=			fairseq_model.state_dict()

	__snake_case      :							List[Any]						=			hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		__snake_case      :							Dict						=			False
		if "conv_layers" in name:
			load_conv_layer(
			    UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							hf_model.config.feat_extract_norm == 'group'      ,							)
			__snake_case      :							Optional[int]						=			True
		else:
			for key, mapped_key in MAPPING.items():
				__snake_case      :							List[str]						=			'sew.' + 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]:
					__snake_case      :							str						=			True
					if "*" in mapped_key:
						__snake_case      :							Optional[int]						=			name.split(UpperCAmelCase_  )[0].split('.'  )[-2]
						__snake_case      :							Optional[int]						=			mapped_key.replace('*'      ,							UpperCAmelCase_  )
					if "weight_g" in name:
						__snake_case      :							int						=			'weight_g'
					elif "weight_v" in name:
						__snake_case      :							Any						=			'weight_v'
					elif "weight" in name:
						__snake_case      :							Union[str, Any]						=			'weight'
					elif "bias" in name:
						__snake_case      :							List[Any]						=			'bias'
					else:
						__snake_case      :							List[str]						=			None
					set_recursively(UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )
				continue
		if not is_used:
			unused_weights.append(UpperCAmelCase_  )

	logger.warning(F"Unused weights: {unused_weights}"  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    str      ,							UpperCAmelCase_   :    int      ,							UpperCAmelCase_   :    Optional[int]      ,							UpperCAmelCase_   :    Union[str, Any]      ,							UpperCAmelCase_   :    Dict  )							->      str:

	'''simple docstring'''





	__snake_case      :							str						=			full_name.split('conv_layers.'  )[-1]
	__snake_case      :							int						=			name.split('.'  )
	__snake_case      :							int						=			int(items[0]  )
	__snake_case      :							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."
			)
			__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."
			)
			__snake_case      :							int						=			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."
			)
			__snake_case      :							Optional[int]						=			value
			logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F"{full_name} has size {value.shape}, but"
			    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
			)
			__snake_case      :							Any						=			value
			logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
	else:
		unused_weights.append(UpperCAmelCase_  )
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Any      ,							UpperCAmelCase_   :    Dict  )							->      Any:

	'''simple docstring'''





	__snake_case      :							Union[str, Any]						=			SEWConfig()
	if is_finetuned:
		__snake_case      :							int						=			model.wav_encoder.wav_model.cfg
	else:
		__snake_case      :							List[Any]						=			model.cfg

	__snake_case      :							Optional[Any]						=			fs_config.conv_bias
	__snake_case      :							Any						=			eval(fs_config.conv_feature_layers  )
	__snake_case      :							str						=			[x[0] for x in conv_layers]
	__snake_case      :							Dict						=			[x[1] for x in conv_layers]
	__snake_case      :							List[Any]						=			[x[2] for x in conv_layers]
	__snake_case      :							Any						=			'gelu'
	__snake_case      :							int						=			'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	__snake_case      :							Any						=			0.0
	__snake_case      :							Optional[Any]						=			fs_config.activation_fn.name
	__snake_case      :							Any						=			fs_config.encoder_embed_dim
	__snake_case      :							Tuple						=			0.02
	__snake_case      :							int						=			fs_config.encoder_ffn_embed_dim
	__snake_case      :							str						=			1E-5
	__snake_case      :							Any						=			fs_config.encoder_layerdrop
	__snake_case      :							int						=			fs_config.encoder_attention_heads
	__snake_case      :							Optional[Any]						=			fs_config.conv_pos_groups
	__snake_case      :							List[str]						=			fs_config.conv_pos
	__snake_case      :							List[str]						=			len(UpperCAmelCase_  )
	__snake_case      :							int						=			fs_config.encoder_layers
	__snake_case      :							int						=			fs_config.squeeze_factor

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

	__snake_case      :							Tuple						=			'Wav2Vec2FeatureExtractor'
	__snake_case      :							List[Any]						=			'Wav2Vec2CTCTokenizer'

	return config
@torch.no_grad()
def   __UpperCAmelCase    (   UpperCAmelCase_   :    Dict      ,							UpperCAmelCase_   :    Optional[int]      ,							UpperCAmelCase_   :    Optional[Any]=None      ,							UpperCAmelCase_   :    Tuple=None      ,							UpperCAmelCase_   :    Dict=True  )							->      Union[str, Any]:

	'''simple docstring'''





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

	if config_path is not None:
		__snake_case      :							List[str]						=			SEWConfig.from_pretrained(UpperCAmelCase_  )
	else:
		__snake_case      :							Optional[Any]						=			convert_config(model[0]      ,							UpperCAmelCase_  )
	__snake_case      :							Dict						=			model[0].eval()

	__snake_case      :							List[str]						=			True if config.feat_extract_norm == 'layer' else False
	__snake_case      :							Tuple						=			WavaVecaFeatureExtractor(
	    feature_size=1      ,							sampling_rate=1_60_00      ,							padding_value=0      ,							do_normalize=UpperCAmelCase_      ,							return_attention_mask=UpperCAmelCase_      ,							)

	if is_finetuned:
		if dict_path:
			__snake_case      :							Optional[Any]						=			Dictionary.load(UpperCAmelCase_  )

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

		__snake_case      :							int						=			SEWForCTC(UpperCAmelCase_  )
	else:
		__snake_case      :							str						=			SEWModel(UpperCAmelCase_  )
		feature_extractor.save_pretrained(UpperCAmelCase_  )

	recursively_load_weights(UpperCAmelCase_      ,							UpperCAmelCase_      ,							UpperCAmelCase_  )

	hf_model.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
	_a					:					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"
	)
	_a					:					List[Any]=			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 json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


_a					:					List[Any]=			logging.get_logger(__name__)

_a					:					Any=			{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

_a					:					int=			{
    "vocab_file": {
        "junnyu/roformer_chinese_small": "https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_base": "https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt",
        "junnyu/roformer_chinese_char_small": (
            "https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_chinese_char_base": (
            "https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_discriminator": (
            "https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt"
        ),
        "junnyu/roformer_small_generator": (
            "https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt"
        ),
    }
}

_a					:					Optional[Any]=			{
    "junnyu/roformer_chinese_small": 1_536,
    "junnyu/roformer_chinese_base": 1_536,
    "junnyu/roformer_chinese_char_small": 512,
    "junnyu/roformer_chinese_char_base": 512,
    "junnyu/roformer_small_discriminator": 128,
    "junnyu/roformer_small_generator": 128,
}


_a					:					str=			{
    "junnyu/roformer_chinese_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_base": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_small": {"do_lower_case": True},
    "junnyu/roformer_chinese_char_base": {"do_lower_case": True},
    "junnyu/roformer_small_discriminator": {"do_lower_case": True},
    "junnyu/roformer_small_generator": {"do_lower_case": True},
}

class 		UpperCamelCase      (   lowercase						):
				UpperCAmelCase    :				Union[str, Any]   =		VOCAB_FILES_NAMES
				UpperCAmelCase    :				Optional[int]   =		PRETRAINED_VOCAB_FILES_MAP
				UpperCAmelCase    :				List[str]   =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCAmelCase    :				int   =		PRETRAINED_INIT_CONFIGURATION
				UpperCAmelCase    :				Dict   =		RoFormerTokenizer



				def __init__(self    :				List[Any]     ,  _A    :				Any=None     ,  _A    :				int=None     ,  _A    :				Dict=True     ,  _A    :				List[Any]="[UNK]"     ,  _A    :				Tuple="[SEP]"     ,  _A    :				List[Any]="[PAD]"     ,  _A    :				str="[CLS]"     ,  _A    :				int="[MASK]"     ,  _A    :				Optional[int]=True     ,  _A    :				List[str]=None     ,  **_A    :				int     ,  )   ->     Dict:
					super().__init__(
					    _A     ,  tokenizer_file=_A     ,  do_lower_case=_A     ,  unk_token=_A     ,  sep_token=_A     ,  pad_token=_A     ,  cls_token=_A     ,  mask_token=_A     ,  tokenize_chinese_chars=_A     ,  strip_accents=_A     ,  **_A     ,  )

					__snake_case      :							List[Any]						=			json.loads(self.backend_tokenizer.normalizer.__getstate__())
					if (
					    pre_tok_state.get('lowercase'     ,  _A) != do_lower_case
					    or pre_tok_state.get('strip_accents'     ,  _A) != strip_accents
					):
						__snake_case      :							Union[str, Any]						=			getattr(_A     ,  pre_tok_state.pop('type'))
						__snake_case      :							Union[str, Any]						=			do_lower_case
						__snake_case      :							str						=			strip_accents
						__snake_case      :							Optional[int]						=			pre_tok_class(**_A)

					__snake_case      :							int						=			do_lower_case



				def __getstate__(self    :				Optional[Any])   ->     Dict:
					__snake_case      :							Optional[int]						=			self.__dict__.copy()
					__snake_case      :							int						=			BertPreTokenizer()
					return state



				def __setstate__(self    :				Optional[Any]     ,  _A    :				Optional[Any])   ->     Dict:
					__snake_case      :							List[str]						=			d
					__snake_case      :							str						=			self.__dict__['_tokenizer'].get_vocab()
					__snake_case      :							int						=			PreTokenizer.custom(JiebaPreTokenizer(_A))



				def  _lowercase					(self    :				int     ,  _A    :				Tuple     ,  _A    :				Any=None)   ->     str:
					__snake_case      :							Dict						=			[self.cls_token_id] + token_ids_a + [self.sep_token_id]

					if token_ids_a:
						output += token_ids_a + [self.sep_token_id]

					return output



				def  _lowercase					(self    :				List[str]     ,  _A    :				List[int]     ,  _A    :				Optional[List[int]] = None)   ->     List[int]:
					__snake_case      :							Tuple						=			[self.sep_token_id]
					__snake_case      :							Optional[Any]						=			[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) * [0] + len(token_ids_a + sep) * [1]



				def  _lowercase					(self    :				List[Any]     ,  _A    :				str     ,  _A    :				Optional[str] = None)   ->     Tuple[str]:
					__snake_case      :							List[Any]						=			self._tokenizer.model.save(_A     ,  name=_A)
					return tuple(_A)






				def  _lowercase					(self    :				int     ,  _A    :				Optional[int]     ,  _A    :				Tuple=None     ,  _A    :				Tuple=None     ,  _A    :				Dict=False     ,  **_A    :				Optional[int]     ,  )   ->     Optional[Any]:
					__snake_case      :							Optional[Any]						=			BertPreTokenizer()
					return super().save_pretrained(_A     ,  _A     ,  _A     ,  _A     ,  **_A)





