






"""simple docstring"""





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__					: Optional[int]=	logging.get_logger(__name__)

A__					: Dict=	{
    """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__					: int=	[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """projector""",
    """classifier""",
]







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	Optional[Any]:
    """simple docstring"""

    UpperCamelCase__         =    {}
    with open(SCREAMING_SNAKE_CASE ,	'r'					) as file:
        for line_number, line in enumerate(SCREAMING_SNAKE_CASE					):
            UpperCamelCase__         =    line.strip()
            if line:
                UpperCamelCase__         =    line.split()
                UpperCamelCase__         =    line_number
                UpperCamelCase__         =    words[0]
                UpperCamelCase__         =    value
    return result







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Dict:
    """simple docstring"""

    for attribute in key.split('.'					):
        UpperCamelCase__         =    getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(SCREAMING_SNAKE_CASE					):
            UpperCamelCase__         =    PARAM_MAPPING[full_name.split('.'					)[-1]]
            UpperCamelCase__         =    'param'

    if weight_type is not None and weight_type != "param":
        UpperCamelCase__         =    getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					).shape
    elif weight_type is not None and weight_type == "param":
        UpperCamelCase__         =    hf_pointer
        for attribute in hf_param_name.split('.'					):
            UpperCamelCase__         =    getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
        UpperCamelCase__         =    shape_pointer.shape

        # let's reduce dimension
        UpperCamelCase__         =    value[0]
    else:
        UpperCamelCase__         =    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":
        UpperCamelCase__         =    value
    elif weight_type == "weight_g":
        UpperCamelCase__         =    value
    elif weight_type == "weight_v":
        UpperCamelCase__         =    value
    elif weight_type == "bias":
        UpperCamelCase__         =    value
    elif weight_type == "param":
        for attribute in hf_param_name.split('.'					):
            UpperCamelCase__         =    getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
        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_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Tuple:
    """simple docstring"""

    UpperCamelCase__         =    None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(SCREAMING_SNAKE_CASE					):
            UpperCamelCase__         =    PARAM_MAPPING[full_name.split('.'					)[-1]]
            UpperCamelCase__         =    'param'

    if weight_type is not None and weight_type != "param":
        UpperCamelCase__         =    '.'.join([key, weight_type]					)
    elif weight_type is not None and weight_type == "param":
        UpperCamelCase__         =    '.'.join([key, hf_param_name]					)
    else:
        UpperCamelCase__         =    key

    UpperCamelCase__         =    value if 'lm_head' in full_key else value[0]


A__					: Optional[int]=	{
    """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   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=None					)					->	Optional[int]:
    """simple docstring"""

    UpperCamelCase__         =    False
    for key, mapped_key in MAPPING.items():
        UpperCamelCase__         =    '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]:
            UpperCamelCase__         =    True
            if "*" in mapped_key:
                UpperCamelCase__         =    name.split(SCREAMING_SNAKE_CASE					)[0].split('.'					)[-2]
                UpperCamelCase__         =    mapped_key.replace('*' ,	SCREAMING_SNAKE_CASE					)
            if "weight_g" in name:
                UpperCamelCase__         =    'weight_g'
            elif "weight_v" in name:
                UpperCamelCase__         =    'weight_v'
            elif "bias" in name:
                UpperCamelCase__         =    'bias'
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                UpperCamelCase__         =    'weight'
            else:
                UpperCamelCase__         =    None
            if hf_dict is not None:
                rename_dict(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            else:
                set_recursively(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            return is_used
    return is_used







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

    UpperCamelCase__         =    []
    UpperCamelCase__         =    fairseq_model.state_dict()

    UpperCamelCase__         =    hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCamelCase__         =    False
        if "conv_layers" in name:
            load_conv_layer(
                SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	hf_model.config.feat_extract_norm == 'group' ,	)
            UpperCamelCase__         =    True
        else:
            UpperCamelCase__         =    load_wavaveca_layer(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE					)

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	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:
            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.'					)
            UpperCamelCase__         =    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.'					)
            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:
            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.'					)
            UpperCamelCase__         =    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.'					)
            UpperCamelCase__         =    value
            logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'					)
    else:
        unused_weights.append(SCREAMING_SNAKE_CASE					)







@torch.no_grad()
def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=None ,	SCREAMING_SNAKE_CASE=True ,	SCREAMING_SNAKE_CASE=False					)					->	Optional[Any]:
    """simple docstring"""

    if config_path is not None:
        UpperCamelCase__         =    WavaVecaConfig.from_pretrained(SCREAMING_SNAKE_CASE					)
    else:
        UpperCamelCase__         =    WavaVecaConfig()

    if is_seq_class:
        UpperCamelCase__         =    read_txt_into_dict(SCREAMING_SNAKE_CASE					)
        UpperCamelCase__         =    idalabel
        UpperCamelCase__         =    WavaVecaForSequenceClassification(SCREAMING_SNAKE_CASE					)
        UpperCamelCase__         =    WavaVecaFeatureExtractor(
            feature_size=1 ,	sampling_rate=1_60_00 ,	padding_value=0 ,	do_normalize=SCREAMING_SNAKE_CASE ,	return_attention_mask=SCREAMING_SNAKE_CASE ,	)
        feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE					)

    elif is_finetuned:
        if dict_path:
            UpperCamelCase__         =    Dictionary.load(SCREAMING_SNAKE_CASE					)

            # 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.eos_index
            UpperCamelCase__         =    len(target_dict.symbols					)
            UpperCamelCase__         =    os.path.join(SCREAMING_SNAKE_CASE ,	'vocab.json'					)
            if not os.path.isdir(SCREAMING_SNAKE_CASE					):
                logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(SCREAMING_SNAKE_CASE					)					)
                return
            os.makedirs(SCREAMING_SNAKE_CASE ,	exist_ok=SCREAMING_SNAKE_CASE					)
            UpperCamelCase__         =    target_dict.indices

            # fairseq has the <pad> and <s> switched
            UpperCamelCase__         =    0
            UpperCamelCase__         =    1
            with open(SCREAMING_SNAKE_CASE ,	'w' ,	encoding='utf-8'					) as vocab_handle:
                json.dump(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            UpperCamelCase__         =    WavaVecaCTCTokenizer(
                SCREAMING_SNAKE_CASE ,	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=SCREAMING_SNAKE_CASE ,	)
            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=SCREAMING_SNAKE_CASE ,	return_attention_mask=SCREAMING_SNAKE_CASE ,	)
            UpperCamelCase__         =    WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE ,	tokenizer=SCREAMING_SNAKE_CASE					)
            processor.save_pretrained(SCREAMING_SNAKE_CASE					)

        UpperCamelCase__         =    WavaVecaForCTC(SCREAMING_SNAKE_CASE					)
    else:
        UpperCamelCase__         =    WavaVecaForPreTraining(SCREAMING_SNAKE_CASE					)

    if is_finetuned or is_seq_class:
        UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path] ,	arg_overrides={'data': '/'.join(dict_path.split('/'					)[:-1]					)}					)
    else:
        UpperCamelCase__         =    argparse.Namespace(task='audio_pretraining'					)
        UpperCamelCase__         =    fairseq.tasks.setup_task(SCREAMING_SNAKE_CASE					)

        UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,	task=SCREAMING_SNAKE_CASE					)

    UpperCamelCase__         =    model[0].eval()

    recursively_load_weights(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	not is_finetuned					)

    hf_wavavec.save_pretrained(SCREAMING_SNAKE_CASE					)


if __name__ == "__main__":
  A__					: Union[str, Any]=	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__					: Tuple=	parser.parse_args()

  A__					: Optional[int]=	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,
  )












"""simple docstring"""





def   lowerCAmelCase_( SCREAMING_SNAKE_CASE					)					->	list[int]:
    """simple docstring"""

    UpperCamelCase__         =    len(SCREAMING_SNAKE_CASE					)
    for i in range(SCREAMING_SNAKE_CASE					):
        for j in range(i + 1 ,	SCREAMING_SNAKE_CASE					):
            if numbers[j] < numbers[i]:
                UpperCamelCase__       ,						UpperCamelCase__         =    numbers[j], numbers[i]
    return numbers


if __name__ == "__main__":
  A__					: Union[str, Any]=	input("""Enter numbers separated by a comma:\n""").strip()
  A__					: List[Any]=	[int(item) for item in user_input.split(""",""")]
  print(exchange_sort(unsorted))




