



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()
_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "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",
}
_snake_case	=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Tuple				     =					{}
							with open(_lowerCamelCase       ,				"r"						) as file:
														for line_number, line in enumerate(_lowerCamelCase						):
																					_lowerCAmelCase  :							Optional[Any]				     =					line.strip()
																					if line:
																												_lowerCAmelCase  :							List[str]				     =					line.split()
																												_lowerCAmelCase  :							Optional[Any]				     =					line_number
																												_lowerCAmelCase  :							List[str]				     =					words[0]
																												_lowerCAmelCase  :							int				     =					value
							return result






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							for attribute in key.split("."						):
														_lowerCAmelCase  :							Dict				     =					getattr(_lowerCamelCase       ,				_lowerCamelCase						)

							_lowerCAmelCase  :							Dict				     =					None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_lowerCamelCase						):
																					_lowerCAmelCase  :							Optional[Any]				     =					PARAM_MAPPING[full_name.split("."						)[-1]]
																					_lowerCAmelCase  :							Union[str, Any]				     =					"param"

							if weight_type is not None and weight_type != "param":
														_lowerCAmelCase  :							Tuple				     =					getattr(_lowerCamelCase       ,				_lowerCamelCase						).shape
							elif weight_type is not None and weight_type == "param":
														_lowerCAmelCase  :							List[str]				     =					hf_pointer
														for attribute in hf_param_name.split("."						):
																					_lowerCAmelCase  :							List[str]				     =					getattr(_lowerCamelCase       ,				_lowerCamelCase						)
														_lowerCAmelCase  :							Any				     =					shape_pointer.shape

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

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					None
							for param_key in PARAM_MAPPING.keys():
														if full_name.endswith(_lowerCamelCase						):
																					_lowerCAmelCase  :							List[str]				     =					PARAM_MAPPING[full_name.split("."						)[-1]]
																					_lowerCAmelCase  :							Dict				     =					"param"

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

							_lowerCAmelCase  :							Optional[int]				     =					value if "lm_head" in full_key else value[0]


_snake_case	=						{
    "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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=None       ,				_lowerCamelCase=None						):





							'''simple docstring'''




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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[str]				     =					[]
							_lowerCAmelCase  :							Union[str, Any]				     =					fairseq_model.state_dict()

							_lowerCAmelCase  :							str				     =					hf_model.wavaveca.feature_extractor

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

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Dict				     =					full_name.split("conv_layers."						)[-1]
							_lowerCAmelCase  :							Dict				     =					name.split("."						)
							_lowerCAmelCase  :							str				     =					int(items[0]						)
							_lowerCAmelCase  :							Optional[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."						)
																					_lowerCAmelCase  :							List[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."						)
																					_lowerCAmelCase  :							Dict				     =					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."						)
																					_lowerCAmelCase  :							Dict				     =					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."						)
																					_lowerCAmelCase  :							List[Any]				     =					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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase=None       ,				_lowerCamelCase=None       ,				_lowerCamelCase=True       ,				_lowerCamelCase=False						):





							'''simple docstring'''




							if config_path is not None:
														_lowerCAmelCase  :							str				     =					WavaVecaConfig.from_pretrained(_lowerCamelCase						)
							else:
														_lowerCAmelCase  :							Tuple				     =					WavaVecaConfig()

							if is_seq_class:
														_lowerCAmelCase  :							List[str]				     =					read_txt_into_dict(_lowerCamelCase						)
														_lowerCAmelCase  :							str				     =					idalabel
														_lowerCAmelCase  :							Union[str, Any]				     =					WavaVecaForSequenceClassification(_lowerCamelCase						)
														_lowerCAmelCase  :							Any				     =					WavaVecaFeatureExtractor(
														    feature_size=1       ,				sampling_rate=16_000       ,				padding_value=0       ,				do_normalize=_lowerCamelCase       ,				return_attention_mask=_lowerCamelCase       ,				)
														feature_extractor.save_pretrained(_lowerCamelCase						)

							elif is_finetuned:
														if dict_path:
																					_lowerCAmelCase  :							int				     =					Dictionary.load(_lowerCamelCase						)

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

																					# fairseq has the <pad> and <s> switched
																					_lowerCAmelCase  :							Optional[Any]				     =					0
																					_lowerCAmelCase  :							Tuple				     =					1
																					with open(_lowerCamelCase       ,				"w"       ,				encoding="utf-8"						) as vocab_handle:
																												json.dump(_lowerCamelCase       ,				_lowerCamelCase						)
																					_lowerCAmelCase  :							List[str]				     =					WavaVecaCTCTokenizer(
																					    _lowerCamelCase       ,				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=_lowerCamelCase       ,				)
																					_lowerCAmelCase  :							int				     =					True if config.feat_extract_norm == "layer" else False
																					_lowerCAmelCase  :							Tuple				     =					WavaVecaFeatureExtractor(
																					    feature_size=1       ,				sampling_rate=16_000       ,				padding_value=0       ,				do_normalize=_lowerCamelCase       ,				return_attention_mask=_lowerCamelCase       ,				)
																					_lowerCAmelCase  :							Optional[Any]				     =					WavaVecaProcessor(feature_extractor=_lowerCamelCase       ,				tokenizer=_lowerCamelCase						)
																					processor.save_pretrained(_lowerCamelCase						)

														_lowerCAmelCase  :							Optional[int]				     =					WavaVecaForCTC(_lowerCamelCase						)
							else:
														_lowerCAmelCase  :							int				     =					WavaVecaForPreTraining(_lowerCamelCase						)

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

														_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Optional[int]				     =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,				task=_lowerCamelCase						)

							_lowerCAmelCase  :							Union[str, Any]				     =					model[0].eval()

							recursively_load_weights(_lowerCamelCase       ,				_lowerCamelCase       ,				not is_finetuned						)

							hf_wavavec.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
						_snake_case	=						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",
						)
						_snake_case	=						parser.parse_args()

						_snake_case	=						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,
						)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'roc_bert'







							def __init__(     self,    __a=3_0522,    __a=768,    __a=12,    __a=12,    __a=3072,    __a="gelu",    __a=0.1,    __a=0.1,    __a=512,    __a=2,    __a=0.02,    __a=1E-12,    __a=True,    __a=0,    __a="absolute",    __a=None,    __a=True,    __a=True,    __a=768,    __a=910,    __a=512,    __a=2_4858,    __a=True,    **__a,    ):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[Any]				     =					vocab_size
														_lowerCAmelCase  :							Optional[Any]				     =					max_position_embeddings
														_lowerCAmelCase  :							Union[str, Any]				     =					hidden_size
														_lowerCAmelCase  :							str				     =					num_hidden_layers
														_lowerCAmelCase  :							List[Any]				     =					num_attention_heads
														_lowerCAmelCase  :							int				     =					intermediate_size
														_lowerCAmelCase  :							Optional[int]				     =					hidden_act
														_lowerCAmelCase  :							Optional[Any]				     =					hidden_dropout_prob
														_lowerCAmelCase  :							Optional[Any]				     =					attention_probs_dropout_prob
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							Optional[Any]				     =					type_vocab_size
														_lowerCAmelCase  :							int				     =					layer_norm_eps
														_lowerCAmelCase  :							Union[str, Any]				     =					use_cache
														_lowerCAmelCase  :							Optional[int]				     =					enable_pronunciation
														_lowerCAmelCase  :							Dict				     =					enable_shape
														_lowerCAmelCase  :							Optional[Any]				     =					pronunciation_embed_dim
														_lowerCAmelCase  :							Any				     =					pronunciation_vocab_size
														_lowerCAmelCase  :							List[str]				     =					shape_embed_dim
														_lowerCAmelCase  :							int				     =					shape_vocab_size
														_lowerCAmelCase  :							Optional[int]				     =					concat_input
														_lowerCAmelCase  :							Dict				     =					position_embedding_type
														_lowerCAmelCase  :							Tuple				     =					classifier_dropout
														super().__init__(pad_token_id=__a,    **__a)
