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()
lowerCAmelCase__     						=			logging.get_logger(__name__)

lowerCAmelCase__     						=			{
    '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 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
							for attribute in key.split('.'     ):
														lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )

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

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
							lowerCAmelCase__					     = []
							lowerCAmelCase__					     = fairseq_model.state_dict()

							lowerCAmelCase__					     = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

							for name, value in fairseq_dict.items():
														lowerCAmelCase__					     = False
														if "conv_layers" in name:
																					load_conv_layer(
																					    lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  hf_model.config.feat_extract_norm == 'group'     ,  )
																					lowerCAmelCase__					     = True
														else:
																					for key, mapped_key in MAPPING.items():
																												lowerCAmelCase__					     = "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]:
																																			lowerCAmelCase__					     = True
																																			if "*" in mapped_key:
																																										lowerCAmelCase__					     = name.split(lowerCAmelCase__     )[0].split('.'     )[-2]
																																										lowerCAmelCase__					     = mapped_key.replace('*'     ,  lowerCAmelCase__     )
																																			if "weight_g" in name:
																																										lowerCAmelCase__					     = "weight_g"
																																			elif "weight_v" in name:
																																										lowerCAmelCase__					     = "weight_v"
																																			elif "weight" in name:
																																										lowerCAmelCase__					     = "weight"
																																			elif "bias" in name:
																																										lowerCAmelCase__					     = "bias"
																																			else:
																																										lowerCAmelCase__					     = 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		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
							lowerCAmelCase__					     = full_name.split('conv_layers.'     )[-1]
							lowerCAmelCase__					     = name.split('.'     )
							lowerCAmelCase__					     = int(items[0]     )
							lowerCAmelCase__					     = 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."""
																					)
																					lowerCAmelCase__					     = 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."""
																					)
																					lowerCAmelCase__					     = 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."
																					)
																					lowerCAmelCase__					     = 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."""
																					)
																					lowerCAmelCase__					     = value
																					logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
							else:
														unused_weights.append(lowerCAmelCase__     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
							lowerCAmelCase__					     = SEWConfig()
							if is_finetuned:
														lowerCAmelCase__					     = model.wav_encoder.wav_model.cfg
							else:
														lowerCAmelCase__					     = model.cfg

							lowerCAmelCase__					     = fs_config.conv_bias
							lowerCAmelCase__					     = eval(fs_config.conv_feature_layers     )
							lowerCAmelCase__					     = [x[0] for x in conv_layers]
							lowerCAmelCase__					     = [x[1] for x in conv_layers]
							lowerCAmelCase__					     = [x[2] for x in conv_layers]
							lowerCAmelCase__					     = "gelu"
							lowerCAmelCase__					     = "layer" if fs_config.extractor_mode == "layer_norm" else "group"
							lowerCAmelCase__					     = 0.0
							lowerCAmelCase__					     = fs_config.activation_fn.name
							lowerCAmelCase__					     = fs_config.encoder_embed_dim
							lowerCAmelCase__					     = 0.0_2
							lowerCAmelCase__					     = fs_config.encoder_ffn_embed_dim
							lowerCAmelCase__					     = 1e-5
							lowerCAmelCase__					     = fs_config.encoder_layerdrop
							lowerCAmelCase__					     = fs_config.encoder_attention_heads
							lowerCAmelCase__					     = fs_config.conv_pos_groups
							lowerCAmelCase__					     = fs_config.conv_pos
							lowerCAmelCase__					     = len(lowerCAmelCase__     )
							lowerCAmelCase__					     = fs_config.encoder_layers
							lowerCAmelCase__					     = fs_config.squeeze_factor

							# take care of any params that are overridden by the Wav2VecCtc model
							if is_finetuned:
														lowerCAmelCase__					     = model.cfg
														lowerCAmelCase__					     = fs_config.final_dropout
														lowerCAmelCase__					     = fs_config.layerdrop
							lowerCAmelCase__					     = fs_config.activation_dropout
							lowerCAmelCase__					     = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
							lowerCAmelCase__					     = fs_config.attention_dropout
							lowerCAmelCase__					     = fs_config.dropout_input
							lowerCAmelCase__					     = fs_config.dropout
							lowerCAmelCase__					     = fs_config.mask_channel_length
							lowerCAmelCase__					     = fs_config.mask_channel_prob
							lowerCAmelCase__					     = fs_config.mask_length
							lowerCAmelCase__					     = fs_config.mask_prob

							lowerCAmelCase__					     = "Wav2Vec2FeatureExtractor"
							lowerCAmelCase__					     = "Wav2Vec2CTCTokenizer"

							return config






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=True     ):

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

							if config_path is not None:
														lowerCAmelCase__					     = SEWConfig.from_pretrained(lowerCAmelCase__     )
							else:
														lowerCAmelCase__					     = convert_config(model[0]     ,  lowerCAmelCase__     )
							lowerCAmelCase__					     = model[0].eval()

							lowerCAmelCase__					     = True if config.feat_extract_norm == "layer" else False
							lowerCAmelCase__					     = WavaVecaFeatureExtractor(
							    feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )

							if is_finetuned:
														if dict_path:
																					lowerCAmelCase__					     = Dictionary.load(lowerCAmelCase__     )

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					lowerCAmelCase__					     = target_dict.pad_index
																					lowerCAmelCase__					     = target_dict.bos_index
																					lowerCAmelCase__					     = target_dict.pad_index
																					lowerCAmelCase__					     = target_dict.bos_index
																					lowerCAmelCase__					     = target_dict.eos_index
																					lowerCAmelCase__					     = len(target_dict.symbols     )
																					lowerCAmelCase__					     = 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__     )
																					with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as vocab_handle:
																												json.dump(target_dict.indices     ,  lowerCAmelCase__     )
																					lowerCAmelCase__					     = 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__					     = WavaVecaProcessor(feature_extractor=lowerCAmelCase__     ,  tokenizer=lowerCAmelCase__     )
																					processor.save_pretrained(lowerCAmelCase__     )

														lowerCAmelCase__					     = SEWForCTC(lowerCAmelCase__     )
							else:
														lowerCAmelCase__					     = SEWModel(lowerCAmelCase__     )
														feature_extractor.save_pretrained(lowerCAmelCase__     )

							recursively_load_weights(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )

							hf_model.save_pretrained(lowerCAmelCase__     )


if __name__ == "__main__":
				lowerCAmelCase__     						=			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'
				)
				lowerCAmelCase__     						=			parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
				)

import copy
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, Optional, Union
@dataclass
class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= True
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= 1
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= False
 UpperCAmelCase_     			= None
 UpperCAmelCase_     			= None




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        return self.__class__(**{k: copy.deepcopy(lowercase__) for k, v in self.__dict__.items()})
