






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

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


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


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

    lowerCAmelCase__   :Optional[int]      							=				None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(UpperCAmelCase       ):
            lowerCAmelCase__   :List[Any]      							=				PARAM_MAPPING[full_name.split("."       )[-1]]
            lowerCAmelCase__   :Tuple      							=				"param"

    if weight_type is not None and weight_type != "param":
        lowerCAmelCase__   :List[str]      							=				getattr(UpperCAmelCase    ,			UpperCAmelCase       ).shape
    elif weight_type is not None and weight_type == "param":
        lowerCAmelCase__   :Dict      							=				hf_pointer
        for attribute in hf_param_name.split("."       ):
            lowerCAmelCase__   :Union[str, Any]      							=				getattr(UpperCAmelCase    ,			UpperCAmelCase       )
        lowerCAmelCase__   :Optional[Any]      							=				shape_pointer.shape

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

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


def 				snake_case__							(							UpperCAmelCase							:  Any    ,			UpperCAmelCase							:  List[Any]    ,			UpperCAmelCase							:  Optional[Any]    ,			UpperCAmelCase							:  Union[str, Any]    ,			UpperCAmelCase							:  str       ):
    lowerCAmelCase__   :Tuple      							=				None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(UpperCAmelCase       ):
            lowerCAmelCase__   :int      							=				PARAM_MAPPING[full_name.split("."       )[-1]]
            lowerCAmelCase__   :Tuple      							=				"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__   :List[str]      							=				".".join([key, hf_param_name]       )
    else:
        lowerCAmelCase__   :Any      							=				key

    lowerCAmelCase__   :Dict      							=				value if "lm_head" in full_key else value[0]


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


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

    lowerCAmelCase__   :str      							=				hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        lowerCAmelCase__   :Tuple      							=				False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase    ,			hf_model.config.feat_extract_norm == "group"    ,			)
            lowerCAmelCase__   :Optional[int]      							=				True
        else:
            lowerCAmelCase__   :Optional[Any]      							=				load_wavaveca_layer(UpperCAmelCase    ,			UpperCAmelCase    ,			UpperCAmelCase       )
        if not is_used:
            unused_weights.append(UpperCAmelCase       )

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


def 				snake_case__							(							UpperCAmelCase							:  Optional[Any]    ,			UpperCAmelCase							:  Tuple    ,			UpperCAmelCase							:  int    ,			UpperCAmelCase							:  Tuple    ,			UpperCAmelCase							:  Any       ):
    lowerCAmelCase__   :Union[str, Any]      							=				full_name.split("conv_layers."       )[-1]
    lowerCAmelCase__   :Union[str, Any]      							=				name.split("."       )
    lowerCAmelCase__   :Dict      							=				int(items[0]       )
    lowerCAmelCase__   :Dict      							=				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__   :Dict      							=				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__   :List[Any]      							=				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__   :Any      							=				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__   :str      							=				value
            logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
    else:
        unused_weights.append(UpperCAmelCase       )


@torch.no_grad()
def 				snake_case__							(							UpperCAmelCase							:  Optional[int]    ,			UpperCAmelCase							:  List[str]    ,			UpperCAmelCase							:  List[Any]=None    ,			UpperCAmelCase							:  Optional[Any]=None    ,			UpperCAmelCase							:  Tuple=True    ,			UpperCAmelCase							:  Optional[int]=False       ):
    if config_path is not None:
        lowerCAmelCase__   :List[Any]      							=				WavaVecaConfig.from_pretrained(UpperCAmelCase       )
    else:
        lowerCAmelCase__   :str      							=				WavaVecaConfig()

    if is_seq_class:
        lowerCAmelCase__   :int      							=				read_txt_into_dict(UpperCAmelCase       )
        lowerCAmelCase__   :Optional[int]      							=				idalabel
        lowerCAmelCase__   :Optional[int]      							=				WavaVecaForSequenceClassification(UpperCAmelCase       )
        lowerCAmelCase__   :Optional[int]      							=				WavaVecaFeatureExtractor(
            feature_size=1    ,			sampling_rate=1_6_0_0_0    ,			padding_value=0    ,			do_normalize=UpperCAmelCase    ,			return_attention_mask=UpperCAmelCase    ,			)
        feature_extractor.save_pretrained(UpperCAmelCase       )

    elif is_finetuned:
        if dict_path:
            lowerCAmelCase__   :Dict      							=				Dictionary.load(UpperCAmelCase       )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            lowerCAmelCase__   :List[str]      							=				target_dict.pad_index
            lowerCAmelCase__   :int      							=				target_dict.bos_index
            lowerCAmelCase__   :Any      							=				target_dict.eos_index
            lowerCAmelCase__   :Dict      							=				len(target_dict.symbols       )
            lowerCAmelCase__   :str      							=				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       )
            lowerCAmelCase__   :List[str]      							=				target_dict.indices

            # fairseq has the <pad> and <s> switched
            lowerCAmelCase__   :Optional[Any]      							=				0
            lowerCAmelCase__   :Optional[Any]      							=				1
            with open(UpperCAmelCase    ,			"w"    ,			encoding="utf-8"       ) as vocab_handle:
                json.dump(UpperCAmelCase    ,			UpperCAmelCase       )
            lowerCAmelCase__   :Optional[Any]      							=				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__   :Union[str, Any]      							=				True if config.feat_extract_norm == "layer" else False
            lowerCAmelCase__   :Optional[int]      							=				WavaVecaFeatureExtractor(
                feature_size=1    ,			sampling_rate=1_6_0_0_0    ,			padding_value=0    ,			do_normalize=UpperCAmelCase    ,			return_attention_mask=UpperCAmelCase    ,			)
            lowerCAmelCase__   :int      							=				WavaVecaProcessor(feature_extractor=UpperCAmelCase    ,			tokenizer=UpperCAmelCase       )
            processor.save_pretrained(UpperCAmelCase       )

        lowerCAmelCase__   :Tuple      							=				WavaVecaForCTC(UpperCAmelCase       )
    else:
        lowerCAmelCase__   :Optional[int]      							=				WavaVecaForPreTraining(UpperCAmelCase       )

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

        lowerCAmelCase__   :int      							=				fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,			task=UpperCAmelCase       )

    lowerCAmelCase__   :str      							=				model[0].eval()

    recursively_load_weights(UpperCAmelCase    ,			UpperCAmelCase    ,			not is_finetuned       )

    hf_wavavec.save_pretrained(UpperCAmelCase       )


if __name__ == "__main__":
 _a    :   List[str]        =			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""",
 )
 _a    :   Optional[Any]        =			parser.parse_args()

 _a    :   str        =			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 math import isqrt, loga


def 				snake_case__							(							UpperCAmelCase							:  int       ):
    lowerCAmelCase__   :Optional[int]      							=				[True] * max_number
    for i in range(2    ,			isqrt(max_number - 1       ) + 1       ):
        if is_prime[i]:
            for j in range(i**2    ,			UpperCAmelCase    ,			UpperCAmelCase       ):
                lowerCAmelCase__   :int      							=				False

    return [i for i in range(2    ,			UpperCAmelCase       ) if is_prime[i]]


def 				snake_case__							(							UpperCAmelCase							:  int = 8_0_0_8_0_0    ,			UpperCAmelCase							:  int = 8_0_0_8_0_0       ):
    lowerCAmelCase__   :str      							=				degree * loga(UpperCAmelCase       )
    lowerCAmelCase__   :Any      							=				int(UpperCAmelCase       )
    lowerCAmelCase__   :List[Any]      							=				calculate_prime_numbers(UpperCAmelCase       )

    lowerCAmelCase__   :List[str]      							=				0
    lowerCAmelCase__   :List[str]      							=				0
    lowerCAmelCase__   :Optional[Any]      							=				len(UpperCAmelCase       ) - 1
    while left < right:
        while (
            prime_numbers[right] * loga(prime_numbers[left]       )
            + prime_numbers[left] * loga(prime_numbers[right]       )
            > upper_bound
        ):
            right -= 1
        hybrid_integers_count += right - left
        left += 1

    return hybrid_integers_count


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



