




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowercase__				:     List[str] 		=						{
    "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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
lowercase__				:     Optional[Any] 		=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





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

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

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


def 						lowerCamelCase__    (		_A     ,	_A							):







	'''simple docstring'''





	snake_case_		    =     []
	snake_case_		    =     fairseq_model.state_dict()

	snake_case_		    =     hf_model.unispeech_sat.feature_extractor

	for name, value in fairseq_dict.items():
		snake_case_		    =     False
		if "conv_layers" in name:
			load_conv_layer(
			    lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__     ,	hf_model.config.feat_extract_norm == "group"     ,	)
			snake_case_		    =     True
		else:
			for key, mapped_key in MAPPING.items():
				snake_case_		    =     '''unispeech_sat.''' + 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]:
					if "layer_norm_for_extract" in name and (".".join(name.split("."							)[:-1]							) != key):
						# special case since naming is very similar
						continue
					snake_case_		    =     True
					if "*" in mapped_key:
						snake_case_		    =     name.split(lowerCAmelCase__							)[0].split("."							)[-2]
						snake_case_		    =     mapped_key.replace("*"     ,	lowerCAmelCase__							)
					if "weight_g" in name:
						snake_case_		    =     '''weight_g'''
					elif "weight_v" in name:
						snake_case_		    =     '''weight_v'''
					elif "bias" in name:
						snake_case_		    =     '''bias'''
					elif "weight" in name:
						# TODO: don't match quantizer.weight_proj
						snake_case_		    =     '''weight'''
					else:
						snake_case_		    =     None
					set_recursively(lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__     ,	lowerCAmelCase__							)
				continue
		if not is_used:
			unused_weights.append(lowerCAmelCase__							)

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


def 						lowerCamelCase__    (		_A     ,	_A     ,	_A     ,	_A     ,	_A							):







	'''simple docstring'''





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


@torch.no_grad()
def 						lowerCamelCase__    (		_A     ,	_A     ,	_A=None     ,	_A=None     ,	_A=True							):







	'''simple docstring'''





	if config_path is not None:
		snake_case_		    =     UniSpeechSatConfig.from_pretrained(lowerCAmelCase__							)
	else:
		snake_case_		    =     UniSpeechSatConfig()

	snake_case_		    =     ''''''

	if is_finetuned:
		snake_case_		    =     UniSpeechSatForCTC(lowerCAmelCase__							)
	else:
		snake_case_		    =     UniSpeechSatForPreTraining(lowerCAmelCase__							)

	snake_case_		    =     fairseq.checkpoint_utils.load_model_ensemble_and_task(
	    [checkpoint_path]     ,	arg_overrides={"data": "/".join(dict_path.split("/"							)[:-1]							)}							)
	snake_case_		    =     model[0].eval()

	recursively_load_weights(lowerCAmelCase__     ,	lowerCAmelCase__							)

	hf_wavavec.save_pretrained(lowerCAmelCase__							)


if __name__ == "__main__":
		lowercase__				:     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"
		)
		lowercase__				:     Union[str, Any] 		=						parser.parse_args()
		convert_unispeech_sat_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)


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


lowercase__       =logging.get_logger(__name__)

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

lowercase__       ={
    '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'
        ),
    }
}

lowercase__       ={
    '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,
}


lowercase__       ={
    '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    ):
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      VOCAB_FILES_NAMES
   _SCREAMING_SNAKE_CASE      :							List[Any]	       =      PRETRAINED_VOCAB_FILES_MAP
   _SCREAMING_SNAKE_CASE      :							List[str]	       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _SCREAMING_SNAKE_CASE      :							Optional[Any]	       =      PRETRAINED_INIT_CONFIGURATION
   _SCREAMING_SNAKE_CASE      :							Optional[int]	       =      RoFormerTokenizer
   def __init__(self :					List[str]					,				snake_case_ :					Optional[int]=None					,				snake_case_ :					str=None					,				snake_case_ :					Optional[Any]=True					,				snake_case_ :					str="[UNK]"					,				snake_case_ :					Dict="[SEP]"					,				snake_case_ :					Any="[PAD]"					,				snake_case_ :					str="[CLS]"					,				snake_case_ :					List[Any]="[MASK]"					,				snake_case_ :					Any=True					,				snake_case_ :					List[str]=None					,				**snake_case_ :					Optional[int]					,				):
    super().__init__(
        snake_case_					,				tokenizer_file=snake_case_					,				do_lower_case=snake_case_					,				unk_token=snake_case_					,				sep_token=snake_case_					,				pad_token=snake_case_					,				cls_token=snake_case_					,				mask_token=snake_case_					,				tokenize_chinese_chars=snake_case_					,				strip_accents=snake_case_					,				**snake_case_					,				)

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

    __a :  Optional[Any]						=  do_lower_case
   def __getstate__(self :					Union[str, Any]   ):
    __a :  Any						=  self.__dict__.copy()
    __a :  Union[str, Any]						=  BertPreTokenizer()
    return state
   def __setstate__(self :					Tuple					,				snake_case_ :					Optional[Any]   ):
    __a :  Dict						=  d
    __a :  str						=  self.__dict__['''_tokenizer'''].get_vocab()
    __a :  Optional[Any]						=  PreTokenizer.custom(JiebaPreTokenizer(snake_case_   )   )
   def       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[Any]					,				snake_case_ :					Optional[Any]=None   ):
    __a :  Optional[int]						=  [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       lowerCAmelCase			(self :					Optional[int]					,				snake_case_ :					List[int]					,				snake_case_ :					Optional[List[int]] = None   ):
    __a :  int						=  [self.sep_token_id]
    __a :  List[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       lowerCAmelCase			(self :					int					,				snake_case_ :					str					,				snake_case_ :					Optional[str] = None   ):
    __a :  Optional[Any]						=  self._tokenizer.model.save(snake_case_					,				name=snake_case_   )
    return tuple(snake_case_   )







   def       lowerCAmelCase			(self :					Dict					,				snake_case_ :					Dict					,				snake_case_ :					Tuple=None					,				snake_case_ :					Optional[Any]=None					,				snake_case_ :					Union[str, Any]=False					,				**snake_case_ :					Tuple					,				):
    __a :  List[str]						=  BertPreTokenizer()
    return super().save_pretrained(snake_case_					,				snake_case_					,				snake_case_					,				snake_case_					,				**snake_case_   )
