






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()
_a    :   Dict        =			logging.get_logger(__name__)

_a    :   Tuple        =			{
    """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 				snake_case__							(							UpperCAmelCase							:  int    ,			UpperCAmelCase							:  Tuple    ,			UpperCAmelCase							:  Union[str, Any]    ,			UpperCAmelCase							:  Any    ,			UpperCAmelCase							:  List[str]       ):
    for attribute in key.split("."       ):
        lowerCAmelCase__   :Optional[Any]      							=				getattr(UpperCAmelCase    ,			UpperCAmelCase       )

    if weight_type is not None:
        lowerCAmelCase__   :int      							=				getattr(UpperCAmelCase    ,			UpperCAmelCase       ).shape
    else:
        lowerCAmelCase__   :List[str]      							=				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__   :Tuple      							=				value
    elif weight_type == "weight_g":
        lowerCAmelCase__   :Tuple      							=				value
    elif weight_type == "weight_v":
        lowerCAmelCase__   :int      							=				value
    elif weight_type == "bias":
        lowerCAmelCase__   :Optional[int]      							=				value
    else:
        lowerCAmelCase__   :Any      							=				value

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


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  List[str]    ,			UpperCAmelCase							:  Dict       ):
    lowerCAmelCase__   :Union[str, Any]      							=				[]
    lowerCAmelCase__   :Optional[Any]      							=				fairseq_model.state_dict()

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

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


def 				snake_case__							(							UpperCAmelCase							:  str    ,			UpperCAmelCase							:  Any       ):
    lowerCAmelCase__   :Union[str, Any]      							=				SEWConfig()
    if is_finetuned:
        lowerCAmelCase__   :str      							=				model.wav_encoder.wav_model.cfg
    else:
        lowerCAmelCase__   :Optional[Any]      							=				model.cfg

    lowerCAmelCase__   :Tuple      							=				fs_config.conv_bias
    lowerCAmelCase__   :List[Any]      							=				eval(fs_config.conv_feature_layers       )
    lowerCAmelCase__   :Dict      							=				[x[0] for x in conv_layers]
    lowerCAmelCase__   :List[str]      							=				[x[1] for x in conv_layers]
    lowerCAmelCase__   :str      							=				[x[2] for x in conv_layers]
    lowerCAmelCase__   :int      							=				"gelu"
    lowerCAmelCase__   :int      							=				"layer" if fs_config.extractor_mode == "layer_norm" else "group"
    lowerCAmelCase__   :List[Any]      							=				0.0
    lowerCAmelCase__   :List[str]      							=				fs_config.activation_fn.name
    lowerCAmelCase__   :Union[str, Any]      							=				fs_config.encoder_embed_dim
    lowerCAmelCase__   :Any      							=				0.02
    lowerCAmelCase__   :Optional[int]      							=				fs_config.encoder_ffn_embed_dim
    lowerCAmelCase__   :Any      							=				1E-5
    lowerCAmelCase__   :Dict      							=				fs_config.encoder_layerdrop
    lowerCAmelCase__   :List[Any]      							=				fs_config.encoder_attention_heads
    lowerCAmelCase__   :str      							=				fs_config.conv_pos_groups
    lowerCAmelCase__   :Union[str, Any]      							=				fs_config.conv_pos
    lowerCAmelCase__   :Optional[int]      							=				len(UpperCAmelCase       )
    lowerCAmelCase__   :List[str]      							=				fs_config.encoder_layers
    lowerCAmelCase__   :int      							=				fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        lowerCAmelCase__   :str      							=				model.cfg
        lowerCAmelCase__   :str      							=				fs_config.final_dropout
        lowerCAmelCase__   :List[str]      							=				fs_config.layerdrop
    lowerCAmelCase__   :Dict      							=				fs_config.activation_dropout
    lowerCAmelCase__   :Union[str, Any]      							=				fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    lowerCAmelCase__   :int      							=				fs_config.attention_dropout
    lowerCAmelCase__   :Tuple      							=				fs_config.dropout_input
    lowerCAmelCase__   :Dict      							=				fs_config.dropout
    lowerCAmelCase__   :List[Any]      							=				fs_config.mask_channel_length
    lowerCAmelCase__   :int      							=				fs_config.mask_channel_prob
    lowerCAmelCase__   :Optional[int]      							=				fs_config.mask_length
    lowerCAmelCase__   :Optional[Any]      							=				fs_config.mask_prob

    lowerCAmelCase__   :Union[str, Any]      							=				"Wav2Vec2FeatureExtractor"
    lowerCAmelCase__   :Union[str, Any]      							=				"Wav2Vec2CTCTokenizer"

    return config


@torch.no_grad()
def 				snake_case__							(							UpperCAmelCase							:  Union[str, Any]    ,			UpperCAmelCase							:  str    ,			UpperCAmelCase							:  Optional[Any]=None    ,			UpperCAmelCase							:  Optional[Any]=None    ,			UpperCAmelCase							:  int=True       ):

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

    if config_path is not None:
        lowerCAmelCase__   :Any      							=				SEWConfig.from_pretrained(UpperCAmelCase       )
    else:
        lowerCAmelCase__   :Optional[int]      							=				convert_config(model[0]    ,			UpperCAmelCase       )
    lowerCAmelCase__   :List[str]      							=				model[0].eval()

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

    if is_finetuned:
        if dict_path:
            lowerCAmelCase__   :List[Any]      							=				Dictionary.load(UpperCAmelCase       )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            lowerCAmelCase__   :Tuple      							=				target_dict.pad_index
            lowerCAmelCase__   :Optional[int]      							=				target_dict.bos_index
            lowerCAmelCase__   :Any      							=				target_dict.pad_index
            lowerCAmelCase__   :Dict      							=				target_dict.bos_index
            lowerCAmelCase__   :Tuple      							=				target_dict.eos_index
            lowerCAmelCase__   :Tuple      							=				len(target_dict.symbols       )
            lowerCAmelCase__   :Tuple      							=				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       )
            lowerCAmelCase__   :str      							=				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    ,			)
            lowerCAmelCase__   :Dict      							=				WavaVecaProcessor(feature_extractor=UpperCAmelCase    ,			tokenizer=UpperCAmelCase       )
            processor.save_pretrained(UpperCAmelCase       )

        lowerCAmelCase__   :Optional[Any]      							=				SEWForCTC(UpperCAmelCase       )
    else:
        lowerCAmelCase__   :Union[str, Any]      							=				SEWModel(UpperCAmelCase       )
        feature_extractor.save_pretrained(UpperCAmelCase       )

    recursively_load_weights(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )

    hf_model.save_pretrained(UpperCAmelCase       )


if __name__ == "__main__":
 _a    :   Optional[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(
     """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
 )
 _a    :   Optional[Any]        =			parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )











def 				snake_case__							(							UpperCAmelCase							:  Tuple       ):
    lowerCAmelCase__   :List[Any]      							=				len(UpperCAmelCase       )
    for i in range(length - 1       ):
        lowerCAmelCase__   :Union[str, Any]      							=				i
        for k in range(i + 1    ,			UpperCAmelCase       ):
            if collection[k] < collection[least]:
                lowerCAmelCase__   :Any      							=				k
        if least != i:
            lowerCAmelCase__							,lowerCAmelCase__   :int      							=				(collection[i], collection[least])
    return collection


if __name__ == "__main__":
 _a    :   Optional[int]        =			input("""Enter numbers separated by a comma:\n""").strip()
 _a    :   Any        =			[int(item) for item in user_input.split(""",""")]
 print(selection_sort(unsorted))



