




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()
a_ 		=  logging.get_logger(__name__)

a_ 		=  {
    """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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[str]:

    """simple docstring"""


    for attribute in key.split('''.'''   ):
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   )

    if weight_type is not None:
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   ).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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_   )							->						Dict:

    """simple docstring"""


    __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(
                lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  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(lowercase_   )[0].split('''.'''   )[-2]
                        if not layer_index.isnumeric():
                            continue
                        __UpperCamelCase			=    mapped_key.replace('''*'''       ,  lowercase_   )
                    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(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )
                continue
        if not is_used:
            unused_weights.append(lowercase_   )

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						List[Any]:

    """simple docstring"""


    __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(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						Optional[int]:

    """simple docstring"""


    __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(lowercase_   )
    __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        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_=None       ,  lowercase_=None       ,  lowercase_=True   )							->						Dict:

    """simple docstring"""


    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(lowercase_   )
    else:
        __UpperCamelCase			=    convert_config(model[0]       ,  lowercase_   )
    __UpperCamelCase			=    model[0].eval()

    __UpperCamelCase			=    True if config.feat_extract_norm == '''layer''' else False
    __UpperCamelCase			=    WavaVecaFeatureExtractor(
        feature_size=1       ,  sampling_rate=1_60_00       ,  padding_value=0       ,  do_normalize=lowercase_       ,  return_attention_mask=lowercase_       ,  )

    if is_finetuned:
        if dict_path:
            __UpperCamelCase			=    Dictionary.load(lowercase_   )

            # 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(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_   )
            with open(lowercase_       ,  '''w'''       ,  encoding='''utf-8'''   ) as vocab_handle:
                json.dump(target_dict.indices       ,  lowercase_   )
            __UpperCamelCase			=    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_       ,  )
            __UpperCamelCase			=    WavaVecaProcessor(feature_extractor=lowercase_       ,  tokenizer=lowercase_   )
            processor.save_pretrained(lowercase_   )

        __UpperCamelCase			=    SEWDForCTC(lowercase_   )
    else:
        __UpperCamelCase			=    SEWDModel(lowercase_   )
        feature_extractor.save_pretrained(lowercase_   )

    recursively_load_weights(lowercase_       ,  lowercase_       ,  lowercase_   )

    hf_model.save_pretrained(lowercase_   )


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













def  A_(      A = 1000 ):
	UpperCAmelCase_		    =  0
	for i in range(A ):
		if i % 3 == 0 or i % 5 == 0:
			result += i
	return result


if __name__ == "__main__":
				print(f"{solution() = }")


