





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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'attention.self.query_proj': 'encoder.encoder.layer.*.attention.self.query_proj',
    'attention.self.key_proj': 'encoder.encoder.layer.*.attention.self.key_proj',
    'attention.self.value_proj': 'encoder.encoder.layer.*.attention.self.value_proj',
    'attention.output.dense': 'encoder.encoder.layer.*.attention.output.dense',
    'attention.output.LayerNorm': 'encoder.encoder.layer.*.attention.output.LayerNorm',
    'intermediate.dense': 'encoder.encoder.layer.*.intermediate.dense',
    'output.dense': 'encoder.encoder.layer.*.output.dense',
    'output.LayerNorm': 'encoder.encoder.layer.*.output.LayerNorm',
    'encoder.encoder.rel_embeddings': 'encoder.encoder.rel_embeddings',
    'encoder.encoder.LayerNorm': 'encoder.encoder.LayerNorm',
    '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_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Optional[int]:
       for attribute in key.split("."	):
              UpperCamelCase_ 							=	getattr(UpperCamelCase_       ,		UpperCamelCase_	)

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

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




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	[]
       UpperCamelCase_ 							=	fairseq_model.state_dict()

       UpperCamelCase_ 							=	hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              UpperCamelCase_ 							=	False
              if "conv_layers" in name:
                     load_conv_layer(
                         UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		hf_model.config.feat_extract_norm == "group"       ,		)
                     UpperCamelCase_ 							=	True
              else:
                     for key, mapped_key in MAPPING.items():
                            UpperCamelCase_ 							=	"sew_d." + 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]:
                                   UpperCamelCase_ 							=	True
                                   if "*" in mapped_key:
                                          UpperCamelCase_ 							=	name.split(UpperCamelCase_	)[0].split("."	)[-2]
                                          if not layer_index.isnumeric():
                                                 continue
                                          UpperCamelCase_ 							=	mapped_key.replace("*"       ,		UpperCamelCase_	)
                                   if "weight_g" in name:
                                          UpperCamelCase_ 							=	"weight_g"
                                   elif "weight_v" in name:
                                          UpperCamelCase_ 							=	"weight_v"
                                   elif "weight" in name:
                                          UpperCamelCase_ 							=	"weight"
                                   elif "bias" in name:
                                          UpperCamelCase_ 							=	"bias"
                                   else:
                                          UpperCamelCase_ 							=	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 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)  ->		Union[str, Any]:
       UpperCamelCase_ 							=	full_name.split("conv_layers."	)[-1]
       UpperCamelCase_ 							=	name.split("."	)
       UpperCamelCase_ 							=	int(items[0]	)
       UpperCamelCase_ 							=	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.'''
                     )
                     UpperCamelCase_ 							=	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.'''
                     )
                     UpperCamelCase_ 							=	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."
                     )
                     UpperCamelCase_ 							=	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.'''
                     )
                     UpperCamelCase_ 							=	value
                     logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''	)
       else:
              unused_weights.append(UpperCamelCase_	)




def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_	)  ->		int:
       UpperCamelCase_ 							=	SEWDConfig()
       if is_finetuned:
              UpperCamelCase_ 							=	model.wav_encoder.wav_model.cfg
       else:
              UpperCamelCase_ 							=	model.cfg

       UpperCamelCase_ 							=	fs_config.conv_bias
       UpperCamelCase_ 							=	eval(fs_config.conv_feature_layers	)
       UpperCamelCase_ 							=	[x[0] for x in conv_layers]
       UpperCamelCase_ 							=	[x[1] for x in conv_layers]
       UpperCamelCase_ 							=	[x[2] for x in conv_layers]
       UpperCamelCase_ 							=	"gelu"
       UpperCamelCase_ 							=	"layer" if fs_config.extractor_mode == "layer_norm" else "group"
       UpperCamelCase_ 							=	0.0
       UpperCamelCase_ 							=	fs_config.activation_fn.name
       UpperCamelCase_ 							=	fs_config.encoder_embed_dim
       UpperCamelCase_ 							=	0.02
       UpperCamelCase_ 							=	fs_config.encoder_ffn_embed_dim
       UpperCamelCase_ 							=	1e-5
       UpperCamelCase_ 							=	fs_config.encoder_layerdrop
       UpperCamelCase_ 							=	fs_config.encoder_attention_heads
       UpperCamelCase_ 							=	fs_config.conv_pos_groups
       UpperCamelCase_ 							=	fs_config.conv_pos
       UpperCamelCase_ 							=	len(UpperCamelCase_	)
       UpperCamelCase_ 							=	fs_config.encoder_layers
       UpperCamelCase_ 							=	fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       UpperCamelCase_ 							=	fs_config.max_position_embeddings
       UpperCamelCase_ 							=	fs_config.position_buckets
       UpperCamelCase_ 							=	fs_config.share_att_key
       UpperCamelCase_ 							=	fs_config.relative_attention
       UpperCamelCase_ 							=	fs_config.position_biased_input
       UpperCamelCase_ 							=	tuple(fs_config.pos_att_type.split("|"	)	)
       UpperCamelCase_ 							=	fs_config.norm_rel_ebd

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

       UpperCamelCase_ 							=	"Wav2Vec2FeatureExtractor"
       UpperCamelCase_ 							=	"Wav2Vec2CTCTokenizer"

       return config




@torch.no_grad()
def 			lowerCAmelCase_			(				UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_=None       ,		UpperCamelCase_=None       ,		UpperCamelCase_=True	)  ->		Union[str, Any]:

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

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

       UpperCamelCase_ 							=	True if config.feat_extract_norm == "layer" else False
       UpperCamelCase_ 							=	WavaVecaFeatureExtractor(
           feature_size=1       ,		sampling_rate=16000       ,		padding_value=0       ,		do_normalize=UpperCamelCase_       ,		return_attention_mask=UpperCamelCase_       ,		)

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

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     UpperCamelCase_ 							=	target_dict.pad_index
                     UpperCamelCase_ 							=	target_dict.bos_index
                     UpperCamelCase_ 							=	target_dict.pad_index
                     UpperCamelCase_ 							=	target_dict.bos_index
                     UpperCamelCase_ 							=	target_dict.eos_index
                     UpperCamelCase_ 							=	len(target_dict.symbols	)
                     UpperCamelCase_ 							=	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_	)
                     UpperCamelCase_ 							=	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_       ,		)
                     UpperCamelCase_ 							=	WavaVecaProcessor(feature_extractor=UpperCamelCase_       ,		tokenizer=UpperCamelCase_	)
                     processor.save_pretrained(UpperCamelCase_	)

              UpperCamelCase_ 							=	SEWDForCTC(UpperCamelCase_	)
       else:
              UpperCamelCase_ 							=	SEWDModel(UpperCamelCase_	)
              feature_extractor.save_pretrained(UpperCamelCase_	)

       recursively_load_weights(UpperCamelCase_       ,		UpperCamelCase_       ,		UpperCamelCase_	)

       hf_model.save_pretrained(UpperCamelCase_	)


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








from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_UpperCAmelCase										=					logging.get_logger(__name__)

_UpperCAmelCase										=					{
    'mnaylor/mega-base-wikitext': 'https://huggingface.co/mnaylor/mega-base-wikitext/resolve/main/config.json',
}



class 				_UpperCamelCase       (						lowerCAmelCase_  ):
       _UpperCamelCase					:       Union[str, Any]       = '''mega'''

       def __init__(		self:			Optional[int]       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=30522       , _SCREAMING_SNAKE_CASE:			Optional[Any]=128       , _SCREAMING_SNAKE_CASE:			Any=4       , _SCREAMING_SNAKE_CASE:			Tuple=256       , _SCREAMING_SNAKE_CASE:			List[str]=16       , _SCREAMING_SNAKE_CASE:			Dict=True       , _SCREAMING_SNAKE_CASE:			Optional[int]=64       , _SCREAMING_SNAKE_CASE:			Union[str, Any]=False       , _SCREAMING_SNAKE_CASE:			List[Any]=-1       , _SCREAMING_SNAKE_CASE:			Tuple=None       , _SCREAMING_SNAKE_CASE:			Optional[Any]=True       , _SCREAMING_SNAKE_CASE:			Optional[int]="scalenorm"       , _SCREAMING_SNAKE_CASE:			int=True       , _SCREAMING_SNAKE_CASE:			List[str]="silu"       , _SCREAMING_SNAKE_CASE:			int="softmax"       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[int]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Tuple=False       , _SCREAMING_SNAKE_CASE:			List[Any]=True       , _SCREAMING_SNAKE_CASE:			Tuple=256       , _SCREAMING_SNAKE_CASE:			Any=True       , _SCREAMING_SNAKE_CASE:			List[Any]=0.1       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2048       , _SCREAMING_SNAKE_CASE:			Any=False       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2       , _SCREAMING_SNAKE_CASE:			List[str]=0.02       , _SCREAMING_SNAKE_CASE:			Tuple=0.2       , _SCREAMING_SNAKE_CASE:			Optional[int]=0.02       , _SCREAMING_SNAKE_CASE:			Tuple=1.0       , _SCREAMING_SNAKE_CASE:			List[str]=1       , _SCREAMING_SNAKE_CASE:			List[Any]=0       , _SCREAMING_SNAKE_CASE:			Optional[Any]=2       , _SCREAMING_SNAKE_CASE:			Optional[int]="rotary"       , _SCREAMING_SNAKE_CASE:			Optional[Any]=None       , _SCREAMING_SNAKE_CASE:			Any=True       , _SCREAMING_SNAKE_CASE:			Optional[Any]=True       , **_SCREAMING_SNAKE_CASE:			Union[str, Any]       , )							->			Optional[Any]:




              """simple docstring"""


              super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE       , bos_token_id=_SCREAMING_SNAKE_CASE       , eos_token_id=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE		)

              UpperCamelCase_ 							=	vocab_size
              UpperCamelCase_ 							=	hidden_size
              UpperCamelCase_ 							=	num_hidden_layers
              UpperCamelCase_ 							=	activation
              UpperCamelCase_ 							=	attention_activation
              UpperCamelCase_ 							=	intermediate_size
              UpperCamelCase_ 							=	ema_projection_size
              UpperCamelCase_ 							=	bidirectional
              UpperCamelCase_ 							=	shared_representation_size
              UpperCamelCase_ 							=	use_chunking
              UpperCamelCase_ 							=	chunk_size
              UpperCamelCase_ 							=	truncation
              UpperCamelCase_ 							=	normalize_before_mega
              UpperCamelCase_ 							=	normalization_type
              UpperCamelCase_ 							=	norm_affine
              UpperCamelCase_ 							=	dropout_prob
              UpperCamelCase_ 							=	hidden_dropout_prob
              UpperCamelCase_ 							=	attention_probs_dropout_prob
              UpperCamelCase_ 							=	use_feature_dropout
              UpperCamelCase_ 							=	use_normalized_ffn
              UpperCamelCase_ 							=	nffn_hidden_size
              UpperCamelCase_ 							=	normalize_before_ffn
              UpperCamelCase_ 							=	nffn_activation_dropout_prob
              UpperCamelCase_ 							=	max_positions
              UpperCamelCase_ 							=	add_token_type_embeddings
              UpperCamelCase_ 							=	type_vocab_size
              UpperCamelCase_ 							=	initializer_range
              UpperCamelCase_ 							=	ema_delta_alpha_range
              UpperCamelCase_ 							=	ema_beta_range
              UpperCamelCase_ 							=	ema_gamma_omega_range
              UpperCamelCase_ 							=	relative_positional_bias
              UpperCamelCase_ 							=	use_cache
              UpperCamelCase_ 							=	classifier_dropout
              UpperCamelCase_ 							=	add_lm_hidden_dense_layer
              UpperCamelCase_ 							=	1  # not used but required by Hugging Face



class 				_UpperCamelCase       (						lowerCAmelCase_  ):

       @property
       def lowercase			(		self:			Optional[int]		)							->			Mapping[str, Mapping[int, str]]:




              """simple docstring"""


              if self.task == "multiple-choice":
                     UpperCamelCase_ 							=	{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase_ 							=	{0: "batch", 1: "sequence"}
              return OrderedDict(
                  [
                      ("input_ids", dynamic_axis),
                      ("attention_mask", dynamic_axis),
                  ]		)

