

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			:					str						=			logging.get_logger(__name__)

__A			:					Any						=			{
    '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			:					Dict						=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]





def 	__a     (      A__			:  Union[str, Any]       ):
    SCREAMING_SNAKE_CASE										=   {}
    with open(A__			,  "r"       ) as file:
        for line_number, line in enumerate(A__       ):
            SCREAMING_SNAKE_CASE										=   line.strip()
            if line:
                SCREAMING_SNAKE_CASE										=   line.split()
                SCREAMING_SNAKE_CASE										=   line_number
                SCREAMING_SNAKE_CASE										=   words[0]
                SCREAMING_SNAKE_CASE										=   value
    return result





def 	__a     (      A__			:  List[Any]			,  A__			:  Dict			,  A__			:  List[str]			,  A__			:  int			,  A__			:  Optional[Any]       ):
    for attribute in key.split("."       ):
        SCREAMING_SNAKE_CASE										=   getattr(A__			,  A__       )

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

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

        # let's reduce dimension
        SCREAMING_SNAKE_CASE										=   value[0]
    else:
        SCREAMING_SNAKE_CASE										=   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":
        SCREAMING_SNAKE_CASE										=   value
    elif weight_type == "weight_g":
        SCREAMING_SNAKE_CASE										=   value
    elif weight_type == "weight_v":
        SCREAMING_SNAKE_CASE										=   value
    elif weight_type == "bias":
        SCREAMING_SNAKE_CASE										=   value
    elif weight_type == "param":
        for attribute in hf_param_name.split("."       ):
            SCREAMING_SNAKE_CASE										=   getattr(A__			,  A__       )
        SCREAMING_SNAKE_CASE										=   value
    else:
        SCREAMING_SNAKE_CASE										=   value

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





def 	__a     (      A__			:  str			,  A__			:  Union[str, Any]			,  A__			:  Any			,  A__			:  List[Any]			,  A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(A__       ):
            SCREAMING_SNAKE_CASE										=   PARAM_MAPPING[full_name.split("."       )[-1]]
            SCREAMING_SNAKE_CASE										=   "param"

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

    SCREAMING_SNAKE_CASE										=   value if "lm_head" in full_key else value[0]


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





def 	__a     (      A__			:  Union[str, Any]			,  A__			:  str			,  A__			:  Optional[int]       ):
    SCREAMING_SNAKE_CASE										=   []
    SCREAMING_SNAKE_CASE										=   fairseq_model.state_dict()

    SCREAMING_SNAKE_CASE										=   hf_model.wavaveca.feature_extractor

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

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





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





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

    if is_seq_class:
        SCREAMING_SNAKE_CASE										=   read_txt_into_dict(A__       )
        SCREAMING_SNAKE_CASE										=   idalabel
        SCREAMING_SNAKE_CASE										=   WavaVecaForSequenceClassification(A__       )
        SCREAMING_SNAKE_CASE										=   WavaVecaFeatureExtractor(
            feature_size=1			,  sampling_rate=16000			,  padding_value=0			,  do_normalize=A__			,  return_attention_mask=A__			,  )
        feature_extractor.save_pretrained(A__       )

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

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            SCREAMING_SNAKE_CASE										=   target_dict.pad_index
            SCREAMING_SNAKE_CASE										=   target_dict.bos_index
            SCREAMING_SNAKE_CASE										=   target_dict.eos_index
            SCREAMING_SNAKE_CASE										=   len(target_dict.symbols       )
            SCREAMING_SNAKE_CASE										=   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__       )
            SCREAMING_SNAKE_CASE										=   target_dict.indices

            # fairseq has the <pad> and <s> switched
            SCREAMING_SNAKE_CASE										=   0
            SCREAMING_SNAKE_CASE										=   1
            with open(A__			,  "w"			,  encoding="utf-8"       ) as vocab_handle:
                json.dump(A__			,  A__       )
            SCREAMING_SNAKE_CASE										=   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__			,  )
            SCREAMING_SNAKE_CASE										=   True if config.feat_extract_norm == "layer" else False
            SCREAMING_SNAKE_CASE										=   WavaVecaFeatureExtractor(
                feature_size=1			,  sampling_rate=16000			,  padding_value=0			,  do_normalize=A__			,  return_attention_mask=A__			,  )
            SCREAMING_SNAKE_CASE										=   WavaVecaProcessor(feature_extractor=A__			,  tokenizer=A__       )
            processor.save_pretrained(A__       )

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

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

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

    SCREAMING_SNAKE_CASE										=   model[0].eval()

    recursively_load_weights(A__			,  A__			,  not is_finetuned       )

    hf_wavavec.save_pretrained(A__       )


if __name__ == "__main__":
    __A			:					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			:					Dict						=			parser.parse_args()

    __A			:					Optional[Any]						=			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 typing import Any

import numpy as np





def 	__a     (      A__			:  np.ndarray       ):
    return np.array_equal(A__			,  matrix.conjugate().T       )





def 	__a     (      A__			:  np.ndarray			,  A__			:  np.ndarray       ):
    SCREAMING_SNAKE_CASE										=   v.conjugate().T
    SCREAMING_SNAKE_CASE										=   v_star.dot(A__       )
    assert isinstance(A__			,  np.ndarray       )
    return (v_star_dot.dot(A__       )) / (v_star.dot(A__       ))





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   np.array([[2, 2 + 1j, 4], [2 - 1j, 3, 1j], [4, -1j, 1]]       )
    SCREAMING_SNAKE_CASE										=   np.array([[1], [2], [3]]       )
    assert is_hermitian(A__       ), F"{a} is not hermitian."
    print(rayleigh_quotient(A__			,  A__       )       )

    SCREAMING_SNAKE_CASE										=   np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]]       )
    assert is_hermitian(A__       ), F"{a} is not hermitian."
    assert rayleigh_quotient(A__			,  A__       ) == float(3       )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    tests()