
'''simple docstring'''
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a_											=						logging.get_logger(__name__)

a_											=						{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "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",
}
a_											=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    for attribute in key.split("."       ):
        snake_case_ :    Dict     			=						getattr(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )

    if weight_type is not None:
        snake_case_ :    str     			=						getattr(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ).shape
    else:
        snake_case_ :    List[Any]     			=						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":
        snake_case_ :    Dict     			=						value
    elif weight_type == "weight_g":
        snake_case_ :    Optional[int]     			=						value
    elif weight_type == "weight_v":
        snake_case_ :    Optional[Any]     			=						value
    elif weight_type == "bias":
        snake_case_ :    Dict     			=						value
    elif weight_type == "running_mean":
        snake_case_ :    Union[str, Any]     			=						value
    elif weight_type == "running_var":
        snake_case_ :    int     			=						value
    elif weight_type == "num_batches_tracked":
        snake_case_ :    List[Any]     			=						value
    elif weight_type == "inv_freq":
        snake_case_ :    Any     			=						value
    else:
        snake_case_ :    Dict     			=						value

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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						[]
    snake_case_ :    List[str]     			=						fairseq_model.state_dict()

    snake_case_ :    List[Any]     			=						hf_model.wavaveca_conformer.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case_ :    Optional[int]     			=						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",		)
            snake_case_ :    List[Any]     			=						True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case_ :    Dict     			=						"wav2vec2_conformer." + 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]:
                    snake_case_ :    Tuple     			=						True
                    if "*" in mapped_key:
                        snake_case_ :    str     			=						name.split(__SCREAMING_SNAKE_CASE       )[0].split("."       )[-2]
                        snake_case_ :    Any     			=						mapped_key.replace("*",		__SCREAMING_SNAKE_CASE       )
                    if "pos_bias_u" in name:
                        snake_case_ :    int     			=						None
                    elif "pos_bias_v" in name:
                        snake_case_ :    Optional[int]     			=						None
                    elif "weight_g" in name:
                        snake_case_ :    Optional[Any]     			=						"weight_g"
                    elif "weight_v" in name:
                        snake_case_ :    int     			=						"weight_v"
                    elif "bias" in name:
                        snake_case_ :    List[str]     			=						"bias"
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        snake_case_ :    Optional[Any]     			=						"weight"
                    elif "running_mean" in name:
                        snake_case_ :    Union[str, Any]     			=						"running_mean"
                    elif "inv_freq" in name:
                        snake_case_ :    Optional[Any]     			=						"inv_freq"
                    elif "running_var" in name:
                        snake_case_ :    Optional[int]     			=						"running_var"
                    elif "num_batches_tracked" in name:
                        snake_case_ :    List[Any]     			=						"num_batches_tracked"
                    else:
                        snake_case_ :    str     			=						None
                    set_recursively(__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       )
                continue
        if not is_used:
            unused_weights.append(__SCREAMING_SNAKE_CASE       )

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

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Any     			=						full_name.split("conv_layers."       )[-1]
    snake_case_ :    List[str]     			=						name.split("."       )
    snake_case_ :    Optional[Any]     			=						int(items[0]       )
    snake_case_ :    Union[str, Any]     			=						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.'       )
            snake_case_ :    List[str]     			=						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.'       )
            snake_case_ :    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.'       )
            snake_case_ :    Dict     			=						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.'       )
            snake_case_ :    Tuple     			=						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 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE,		__SCREAMING_SNAKE_CASE=None,		__SCREAMING_SNAKE_CASE=None,		__SCREAMING_SNAKE_CASE=True       ):
    """simple docstring"""






    if config_path is not None:
        snake_case_ :    Dict     			=						WavaVecaConformerConfig.from_pretrained(__SCREAMING_SNAKE_CASE,		hidden_act="swish"       )
    else:
        snake_case_ :    List[str]     			=						WavaVecaConformerConfig()

    if "rope" in checkpoint_path:
        snake_case_ :    List[Any]     			=						"rotary"

    if is_finetuned:
        if dict_path:
            snake_case_ :    Dict     			=						Dictionary.load(__SCREAMING_SNAKE_CASE       )

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

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

        snake_case_ :    Tuple     			=						WavaVecaConformerForCTC(__SCREAMING_SNAKE_CASE       )
    else:
        snake_case_ :    Tuple     			=						WavaVecaConformerForPreTraining(__SCREAMING_SNAKE_CASE       )

    if is_finetuned:
        snake_case_			, snake_case_			, snake_case_ :    List[Any]     			=						fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path],		arg_overrides={"data": "/".join(dict_path.split("/"       )[:-1]       )}       )
    else:
        snake_case_ :    Optional[int]     			=						argparse.Namespace(task="audio_pretraining"       )
        snake_case_ :    List[str]     			=						fairseq.tasks.setup_task(__SCREAMING_SNAKE_CASE       )

        snake_case_			, snake_case_			, snake_case_ :    Union[str, Any]     			=						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path],		task=__SCREAMING_SNAKE_CASE       )

    snake_case_ :    Optional[int]     			=						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_											=						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"
  )
  a_											=						parser.parse_args()
  convert_wavaveca_conformer_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )






'''simple docstring'''
from __future__ import annotations

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    List[Any]     			=						0.00
    snake_case_ :    int     			=						0
    for resistor in resistors:
        if resistor <= 0:
            snake_case_ :    Dict     			=						f'Resistor at index {index} has a negative or zero value!'
            raise ValueError(__SCREAMING_SNAKE_CASE       )
        first_sum += 1 / float(__SCREAMING_SNAKE_CASE       )
        index += 1
    return 1 / first_sum

def 						UpperCamelCase_     (	__SCREAMING_SNAKE_CASE       ):
    """simple docstring"""






    snake_case_ :    Union[str, Any]     			=						0.00
    snake_case_ :    List[Any]     			=						0
    for resistor in resistors:
        sum_r += resistor
        if resistor < 0:
            snake_case_ :    str     			=						f'Resistor at index {index} has a negative value!'
            raise ValueError(__SCREAMING_SNAKE_CASE       )
        index += 1
    return sum_r


if __name__ == "__main__":
  import doctest

  doctest.testmod()




