

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
A_       : Any         =			logging.get_logger(__name__)

A_       : int         =			{
    'post_extract_proj': 'feature_projection.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.layer_norm': 'encoder.layer_norm',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
A_       : Tuple         =			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]

def 						UpperCamelCase					(lowercase_: Optional[Any]					)     ->      str:
	A__							:    Any        =		{}
	with open(lowercase_    ,      """r"""					) as file:
		for line_number, line in enumerate(lowercase_					):
			A__							:    Any        =		line.strip()
			if line:
				A__							:    List[Any]        =		line.split()
				A__							:    List[Any]        =		line_number
				A__							:    Optional[Any]        =		words[0]
				A__							:    int        =		value
	return result

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: Optional[Any]    ,      lowercase_: Optional[int]    ,      lowercase_: List[str]    ,      lowercase_: int					)     ->      str:
	for attribute in key.split("""."""					):
		A__							:    int        =		getattr(lowercase_    ,      lowercase_					)

	A__							:    List[Any]        =		None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowercase_					):
			A__							:    Optional[int]        =		PARAM_MAPPING[full_name.split("""."""					)[-1]]
			A__							:    Dict        =		"""param"""

	if weight_type is not None and weight_type != "param":
		A__							:    Union[str, Any]        =		getattr(lowercase_    ,      lowercase_					).shape
	elif weight_type is not None and weight_type == "param":
		A__							:    Optional[Any]        =		hf_pointer
		for attribute in hf_param_name.split("""."""					):
			A__							:    Optional[int]        =		getattr(lowercase_    ,      lowercase_					)
		A__							:    Optional[int]        =		shape_pointer.shape

		# let's reduce dimension
		A__							:    Any        =		value[0]
	else:
		A__							:    int        =		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":
		A__							:    Optional[Any]        =		value
	elif weight_type == "weight_g":
		A__							:    Optional[Any]        =		value
	elif weight_type == "weight_v":
		A__							:    List[str]        =		value
	elif weight_type == "bias":
		A__							:    List[str]        =		value
	elif weight_type == "param":
		for attribute in hf_param_name.split("""."""					):
			A__							:    Optional[Any]        =		getattr(lowercase_    ,      lowercase_					)
		A__							:    Any        =		value
	else:
		A__							:    Tuple        =		value

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

def 						UpperCamelCase					(lowercase_: List[str]    ,      lowercase_: List[Any]    ,      lowercase_: Union[str, Any]    ,      lowercase_: List[str]    ,      lowercase_: Union[str, Any]					)     ->      List[str]:
	A__							:    Union[str, Any]        =		None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(lowercase_					):
			A__							:    Optional[Any]        =		PARAM_MAPPING[full_name.split("""."""					)[-1]]
			A__							:    int        =		"""param"""

	if weight_type is not None and weight_type != "param":
		A__							:    str        =		""".""".join([key, weight_type]					)
	elif weight_type is not None and weight_type == "param":
		A__							:    List[str]        =		""".""".join([key, hf_param_name]					)
	else:
		A__							:    Tuple        =		key

	A__							:    List[str]        =		value if """lm_head""" in full_key else value[0]


A_       : Any         =			{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: Optional[int]    ,      lowercase_: int=None    ,      lowercase_: Dict=None					)     ->      str:
	A__							:    List[str]        =		False
	for key, mapped_key in MAPPING.items():
		A__							:    int        =		"""wav2vec2.""" + 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]:
			A__							:    Optional[Any]        =		True
			if "*" in mapped_key:
				A__							:    Union[str, Any]        =		name.split(lowercase_					)[0].split("""."""					)[-2]
				A__							:    List[Any]        =		mapped_key.replace("""*"""    ,      lowercase_					)
			if "weight_g" in name:
				A__							:    Any        =		"""weight_g"""
			elif "weight_v" in name:
				A__							:    Optional[int]        =		"""weight_v"""
			elif "bias" in name:
				A__							:    int        =		"""bias"""
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				A__							:    List[str]        =		"""weight"""
			else:
				A__							:    Optional[Any]        =		None
			if hf_dict is not None:
				rename_dict(lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_					)
			else:
				set_recursively(lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_					)
			return is_used
	return is_used

def 						UpperCamelCase					(lowercase_: Any    ,      lowercase_: Optional[int]    ,      lowercase_: List[Any]					)     ->      Optional[int]:
	A__							:    Tuple        =		[]
	A__							:    str        =		fairseq_model.state_dict()

	A__							:    Tuple        =		hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		A__							:    int        =		False
		if "conv_layers" in name:
			load_conv_layer(
			    lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_    ,      hf_model.config.feat_extract_norm == """group"""    ,      )
			A__							:    Optional[Any]        =		True
		else:
			A__							:    int        =		load_wavaveca_layer(lowercase_    ,      lowercase_    ,      lowercase_					)
		if not is_used:
			unused_weights.append(lowercase_					)

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

def 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: int    ,      lowercase_: Dict    ,      lowercase_: Optional[Any]    ,      lowercase_: int					)     ->      List[Any]:
	A__							:    Tuple        =		full_name.split("""conv_layers."""					)[-1]
	A__							:    Optional[int]        =		name.split("""."""					)
	A__							:    Optional[Any]        =		int(items[0]					)
	A__							:    str        =		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."""					)
			A__							:    int        =		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."""					)
			A__							:    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."""					)
			A__							:    Any        =		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."""					)
			A__							:    List[str]        =		value
			logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""					)
	else:
		unused_weights.append(lowercase_					)

@torch.no_grad()
def 						UpperCamelCase					(lowercase_: str    ,      lowercase_: int    ,      lowercase_: Tuple=None    ,      lowercase_: Tuple=None    ,      lowercase_: List[Any]=True    ,      lowercase_: Any=False					)     ->      int:
	if config_path is not None:
		A__							:    int        =		WavaVecaConfig.from_pretrained(lowercase_					)
	else:
		A__							:    List[str]        =		WavaVecaConfig()

	if is_seq_class:
		A__							:    Dict        =		read_txt_into_dict(lowercase_					)
		A__							:    List[Any]        =		idalabel
		A__							:    str        =		WavaVecaForSequenceClassification(lowercase_					)
		A__							:    Tuple        =		WavaVecaFeatureExtractor(
		    feature_size=1    ,      sampling_rate=16000    ,      padding_value=0    ,      do_normalize=lowercase_    ,      return_attention_mask=lowercase_    ,      )
		feature_extractor.save_pretrained(lowercase_					)

	elif is_finetuned:
		if dict_path:
			A__							:    Tuple        =		Dictionary.load(lowercase_					)

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

			# fairseq has the <pad> and <s> switched
			A__							:    Tuple        =		0
			A__							:    Optional[Any]        =		1
			with open(lowercase_    ,      """w"""    ,      encoding="""utf-8"""					) as vocab_handle:
				json.dump(lowercase_    ,      lowercase_					)
			A__							:    Tuple        =		WavaVecaCTCTokenizer(
			    lowercase_    ,      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=lowercase_    ,      )
			A__							:    Optional[int]        =		True if config.feat_extract_norm == """layer""" else False
			A__							:    List[str]        =		WavaVecaFeatureExtractor(
			    feature_size=1    ,      sampling_rate=16000    ,      padding_value=0    ,      do_normalize=lowercase_    ,      return_attention_mask=lowercase_    ,      )
			A__							:    List[Any]        =		WavaVecaProcessor(feature_extractor=lowercase_    ,      tokenizer=lowercase_					)
			processor.save_pretrained(lowercase_					)

		A__							:    Optional[int]        =		WavaVecaForCTC(lowercase_					)
	else:
		A__							:    int        =		WavaVecaForPreTraining(lowercase_					)

	if is_finetuned or is_seq_class:
		A__   ,   A__   ,   A__							:    str        =		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]    ,      arg_overrides={"""data""": """/""".join(dict_path.split("""/"""					)[:-1]					)}					)
	else:
		A__							:    Tuple        =		argparse.Namespace(task="""audio_pretraining"""					)
		A__							:    List[str]        =		fairseq.tasks.setup_task(lowercase_					)

		A__   ,   A__   ,   A__							:    Union[str, Any]        =		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,      task=lowercase_					)

	A__							:    List[Any]        =		model[0].eval()

	recursively_load_weights(lowercase_    ,      lowercase_    ,      not is_finetuned					)

	hf_wavavec.save_pretrained(lowercase_					)


if __name__ == "__main__":
	A_       : Optional[int]         =			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'
	)
	parser.add_argument(
	    '--is_seq_class',
	    action='store_true',
	    help='Whether the model to convert is a fine-tuned sequence classification model or not',
	)
	A_       : Union[str, Any]         =			parser.parse_args()

	A_       : Tuple         =			not args.not_finetuned and not args.is_seq_class
	convert_wavaveca_checkpoint(
	    args.checkpoint_path,
	    args.pytorch_dump_folder_path,
	    args.config_path,
	    args.dict_path,
	    is_finetuned,
	    args.is_seq_class,
	)






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[str]         =			logging.get_logger(__name__)

A_       : Tuple         =			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

A_       : List[Any]         =			{
    '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_       : Tuple         =			{
    'junnyu/roformer_chinese_small': 1536,
    'junnyu/roformer_chinese_base': 1536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


A_       : Union[str, Any]         =			{
    '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     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   Dict  							=       VOCAB_FILES_NAMES
					UpperCAmelCase__:   Tuple  							=       PRETRAINED_VOCAB_FILES_MAP
					UpperCAmelCase__:   List[str]  							=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					UpperCAmelCase__:   List[Any]  							=       PRETRAINED_INIT_CONFIGURATION
					UpperCAmelCase__:   Optional[int]  							=       RoFormerTokenizer






					def __init__( self    ,       A__=None    ,       A__=None    ,       A__=True    ,       A__="[UNK]"    ,       A__="[SEP]"    ,       A__="[PAD]"    ,       A__="[CLS]"    ,       A__="[MASK]"    ,       A__=True    ,       A__=None    ,       **A__    ,       ):
						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__    ,       )

						A__							:    Tuple        =		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
						):
							A__							:    List[Any]        =		getattr(A__    ,       pre_tok_state.pop("""type"""			)			)
							A__							:    Optional[int]        =		do_lower_case
							A__							:    int        =		strip_accents
							A__							:    str        =		pre_tok_class(**A__			)

						A__							:    Any        =		do_lower_case






					def __getstate__( self			):
						A__							:    int        =		self.__dict__.copy()
						A__							:    Union[str, Any]        =		BertPreTokenizer()
						return state






					def __setstate__( self    ,       A__			):
						A__							:    Union[str, Any]        =		d
						A__							:    Union[str, Any]        =		self.__dict__["""_tokenizer"""].get_vocab()
						A__							:    Dict        =		PreTokenizer.custom(JiebaPreTokenizer(A__			)			)






					def   __A			( self    ,       A__    ,       A__=None			):
						A__							:    Optional[Any]        =		[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   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Tuple        =		[self.sep_token_id]
						A__							:    Tuple        =		[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   __A			( self    ,       A__    ,       A__ = None			):
						A__							:    Any        =		self._tokenizer.model.save(A__    ,       name=A__			)
						return tuple(A__			)






					def   __A			( self    ,       A__    ,       A__=None    ,       A__=None    ,       A__=False    ,       **A__    ,       ):
						A__							:    str        =		BertPreTokenizer()
						return super().save_pretrained(A__    ,       A__    ,       A__    ,       A__    ,       **A__			)



