




'''simple docstring'''
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							:				Optional[int]			     =	logging.get_logger(__name__)

A							:				Optional[int]			     =	{
    """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     snake_case_  (       a__		:     Dict   ,a__		:     Optional[int]   ,a__		:     List[str]   ,a__		:     List[str]   ,a__		:     List[Any]      ):



    """simple docstring"""




    for attribute in key.split("""."""      ):
        __lowercase          =						getattr(a__   ,a__      )

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

    logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'      )
def     snake_case_  (       a__		:     str   ,a__		:     Optional[Any]   ,a__		:     str      ):



    """simple docstring"""




    __lowercase          =						[]
    __lowercase          =						fairseq_model.state_dict()

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

    for name, value in fairseq_dict.items():
        __lowercase          =						False
        if "conv_layers" in name:
            load_conv_layer(
                a__   ,a__   ,a__   ,a__   ,hf_model.config.feat_extract_norm == """group"""   ,)
            __lowercase          =						True
        else:
            for key, mapped_key in MAPPING.items():
                __lowercase          =						"""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]:
                    __lowercase          =						True
                    if "*" in mapped_key:
                        __lowercase          =						name.split(a__      )[0].split("""."""      )[-2]
                        if not layer_index.isnumeric():
                            continue
                        __lowercase          =						mapped_key.replace("""*"""   ,a__      )
                    if "weight_g" in name:
                        __lowercase          =						"""weight_g"""
                    elif "weight_v" in name:
                        __lowercase          =						"""weight_v"""
                    elif "weight" in name:
                        __lowercase          =						"""weight"""
                    elif "bias" in name:
                        __lowercase          =						"""bias"""
                    else:
                        __lowercase          =						None
                    set_recursively(a__   ,a__   ,a__   ,a__   ,a__      )
                continue
        if not is_used:
            unused_weights.append(a__      )

    logger.warning(f'Unused weights: {unused_weights}'      )
def     snake_case_  (       a__		:     Any   ,a__		:     Optional[int]   ,a__		:     Tuple   ,a__		:     int   ,a__		:     List[str]      ):



    """simple docstring"""




    __lowercase          =						full_name.split("""conv_layers."""      )[-1]
    __lowercase          =						name.split("""."""      )
    __lowercase          =						int(items[0]      )
    __lowercase          =						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.'
            )
            __lowercase          =						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.'
            )
            __lowercase          =						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."
            )
            __lowercase          =						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.'
            )
            __lowercase          =						value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
    else:
        unused_weights.append(a__      )
def     snake_case_  (       a__		:     Optional[int]   ,a__		:     Tuple      ):



    """simple docstring"""




    __lowercase          =						SEWDConfig()
    if is_finetuned:
        __lowercase          =						model.wav_encoder.wav_model.cfg
    else:
        __lowercase          =						model.cfg

    __lowercase          =						fs_config.conv_bias
    __lowercase          =						eval(fs_config.conv_feature_layers      )
    __lowercase          =						[x[0] for x in conv_layers]
    __lowercase          =						[x[1] for x in conv_layers]
    __lowercase          =						[x[2] for x in conv_layers]
    __lowercase          =						"""gelu"""
    __lowercase          =						"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
    __lowercase          =						0.0
    __lowercase          =						fs_config.activation_fn.name
    __lowercase          =						fs_config.encoder_embed_dim
    __lowercase          =						0.0_2
    __lowercase          =						fs_config.encoder_ffn_embed_dim
    __lowercase          =						1e-5
    __lowercase          =						fs_config.encoder_layerdrop
    __lowercase          =						fs_config.encoder_attention_heads
    __lowercase          =						fs_config.conv_pos_groups
    __lowercase          =						fs_config.conv_pos
    __lowercase          =						len(a__      )
    __lowercase          =						fs_config.encoder_layers
    __lowercase          =						fs_config.squeeze_factor
    # DeBERTa-specific parameters:
    __lowercase          =						fs_config.max_position_embeddings
    __lowercase          =						fs_config.position_buckets
    __lowercase          =						fs_config.share_att_key
    __lowercase          =						fs_config.relative_attention
    __lowercase          =						fs_config.position_biased_input
    __lowercase          =						tuple(fs_config.pos_att_type.split("""|"""      )      )
    __lowercase          =						fs_config.norm_rel_ebd

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

    __lowercase          =						"""Wav2Vec2FeatureExtractor"""
    __lowercase          =						"""Wav2Vec2CTCTokenizer"""

    return config
@torch.no_grad()
def     snake_case_  (       a__		:     Optional[int]   ,a__		:     Tuple   ,a__		:     Union[str, Any]=None   ,a__		:     Any=None   ,a__		:     Tuple=True      ):



    """simple docstring"""




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

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

    __lowercase          =						True if config.feat_extract_norm == """layer""" else False
    __lowercase          =						WavaVecaFeatureExtractor(
        feature_size=1   ,sampling_rate=1_60_00   ,padding_value=0   ,do_normalize=a__   ,return_attention_mask=a__   ,)

    if is_finetuned:
        if dict_path:
            __lowercase          =						Dictionary.load(a__      )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            __lowercase          =						target_dict.pad_index
            __lowercase          =						target_dict.bos_index
            __lowercase          =						target_dict.pad_index
            __lowercase          =						target_dict.bos_index
            __lowercase          =						target_dict.eos_index
            __lowercase          =						len(target_dict.symbols      )
            __lowercase          =						os.path.join(a__   ,"""vocab.json"""      )
            if not os.path.isdir(a__      ):
                logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(a__      )      )
                return
            os.makedirs(a__   ,exist_ok=a__      )
            with open(a__   ,"""w"""   ,encoding="""utf-8"""      ) as vocab_handle:
                json.dump(target_dict.indices   ,a__      )
            __lowercase          =						WavaVecaCTCTokenizer(
                a__   ,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=a__   ,)
            __lowercase          =						WavaVecaProcessor(feature_extractor=a__   ,tokenizer=a__      )
            processor.save_pretrained(a__      )

        __lowercase          =						SEWDForCTC(a__      )
    else:
        __lowercase          =						SEWDModel(a__      )
        feature_extractor.save_pretrained(a__      )

    recursively_load_weights(a__   ,a__   ,a__      )

    hf_model.save_pretrained(a__      )


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










'''simple docstring'''
A							:				int			     =	(1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
A							:				tuple			     =	(-153, -1, 0, 1.2, 200, "A", [], {}, None)
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False

    # Initialization of sum and number of digits.
    __lowercase          =						0
    __lowercase          =						0
    __lowercase          =						n
    # Calculation of digits of the number
    while temp > 0:
        number_of_digits += 1
        temp //= 10
    # Dividing number into separate digits and find Armstrong number
    __lowercase          =						n
    while temp > 0:
        __lowercase          =						temp % 10
        total += rem**number_of_digits
        temp //= 10
    return n == total
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False

    # Init a "histogram" of the digits
    __lowercase          =						[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    __lowercase          =						0
    __lowercase          =						0
    __lowercase          =						n
    while temp > 0:
        __lowercase    ,__lowercase          =						divmod(a__   ,10      )
        digit_histogram[rem] += 1
        digit_total += 1

    for cnt, i in zip(a__   ,range(len(a__      )      )      ):
        total += cnt * i**digit_total

    return n == total
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False
    __lowercase          =						len(str(a__      )      )  # the power that all digits will be raised to
    # check if sum of each digit multiplied expo times is equal to number
    return n == sum(int(a__      ) ** expo for i in str(a__      )      )
def     snake_case_  (       ):



    """simple docstring"""




    __lowercase          =						int(input("""Enter an integer to see if it is an Armstrong number: """      ).strip()      )
    print(f'{num} is {"" if armstrong_number(a__      ) else "not "}an Armstrong number.'      )
    print(f'{num} is {"" if narcissistic_number(a__      ) else "not "}an Armstrong number.'      )
    print(f'{num} is {"" if pluperfect_number(a__      ) else "not "}an Armstrong number.'      )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




