
"""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()
SCREAMING_SNAKE_CASE_            =    logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_            =    {
    '''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''',
}
SCREAMING_SNAKE_CASE_            =    [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]




def      lowercase__    (       lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       int			,					lowerCAmelCase					:       str			,					lowerCAmelCase					:       Dict			,					lowerCAmelCase					:       Union[str, Any]			)			->   Tuple:






    """simple docstring"""




    for attribute in key.split('.'			):
        UpperCAmelCase          =       getattr(lowerCAmelCase			,					lowerCAmelCase			)

    if weight_type is not None:
        UpperCAmelCase          =       getattr(lowerCAmelCase			,					lowerCAmelCase			).shape
    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 == "running_mean":
        UpperCAmelCase          =       value
    elif weight_type == "running_var":
        UpperCAmelCase          =       value
    elif weight_type == "num_batches_tracked":
        UpperCAmelCase          =       value
    elif weight_type == "inv_freq":
        UpperCAmelCase          =       value
    else:
        UpperCAmelCase          =       value

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




def      lowercase__    (       lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       str			,					lowerCAmelCase					:       List[Any]			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       []
    UpperCAmelCase          =       fairseq_model.state_dict()

    UpperCAmelCase          =       hf_model.wavaveca_conformer.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCAmelCase          =       False
        if "conv_layers" in name:
            load_conv_layer(
                lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			,					hf_model.config.feat_extract_norm == 'group'			,					)
            UpperCAmelCase          =       True
        else:
            for key, mapped_key in MAPPING.items():
                UpperCAmelCase          =       '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]:
                    UpperCAmelCase          =       True
                    if "*" in mapped_key:
                        UpperCAmelCase          =       name.split(lowerCAmelCase			)[0].split('.'			)[-2]
                        UpperCAmelCase          =       mapped_key.replace('*'			,					lowerCAmelCase			)
                    if "pos_bias_u" in name:
                        UpperCAmelCase          =       None
                    elif "pos_bias_v" in name:
                        UpperCAmelCase          =       None
                    elif "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'
                    elif "running_mean" in name:
                        UpperCAmelCase          =       'running_mean'
                    elif "inv_freq" in name:
                        UpperCAmelCase          =       'inv_freq'
                    elif "running_var" in name:
                        UpperCAmelCase          =       'running_var'
                    elif "num_batches_tracked" in name:
                        UpperCAmelCase          =       'num_batches_tracked'
                    else:
                        UpperCAmelCase          =       None
                    set_recursively(lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			,					lowerCAmelCase			)
                continue
        if not is_used:
            unused_weights.append(lowerCAmelCase			)

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




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Union[str, Any]			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       List[str]			,					lowerCAmelCase					:       Dict			)			->   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(lowerCAmelCase			)




@torch.no_grad()
def      lowercase__    (       lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Union[str, Any]			,					lowerCAmelCase					:       Optional[Any]=None			,					lowerCAmelCase					:       Optional[Any]=None			,					lowerCAmelCase					:       Tuple=True			)			->   str:






    """simple docstring"""




    if config_path is not None:
        UpperCAmelCase          =       WavaVecaConformerConfig.from_pretrained(lowerCAmelCase			,					hidden_act='swish'			)
    else:
        UpperCAmelCase          =       WavaVecaConformerConfig()

    if "rope" in checkpoint_path:
        UpperCAmelCase          =       'rotary'

    if is_finetuned:
        if dict_path:
            UpperCAmelCase          =       Dictionary.load(lowerCAmelCase			)

            # 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(lowerCAmelCase			,					'vocab.json'			)
            if not os.path.isdir(lowerCAmelCase			):
                logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowerCAmelCase			)			)
                return
            os.makedirs(lowerCAmelCase			,					exist_ok=lowerCAmelCase			)
            UpperCAmelCase          =       target_dict.indices

            # fairseq has the <pad> and <s> switched
            UpperCAmelCase          =       0
            UpperCAmelCase          =       1
            with open(lowerCAmelCase			,					'w'			,					encoding='utf-8'			) as vocab_handle:
                json.dump(lowerCAmelCase			,					lowerCAmelCase			)
            UpperCAmelCase          =       WavaVecaCTCTokenizer(
                lowerCAmelCase			,					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=lowerCAmelCase			,					)
            UpperCAmelCase          =       True if config.feat_extract_norm == 'layer' else False
            UpperCAmelCase          =       WavaVecaFeatureExtractor(
                feature_size=1			,					sampling_rate=16_000			,					padding_value=0			,					do_normalize=lowerCAmelCase			,					return_attention_mask=lowerCAmelCase			,					)
            UpperCAmelCase          =       WavaVecaProcessor(feature_extractor=lowerCAmelCase			,					tokenizer=lowerCAmelCase			)
            processor.save_pretrained(lowerCAmelCase			)

        UpperCAmelCase          =       WavaVecaConformerForCTC(lowerCAmelCase			)
    else:
        UpperCAmelCase          =       WavaVecaConformerForPreTraining(lowerCAmelCase			)

    if is_finetuned:
        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(lowerCAmelCase			)

        UpperCAmelCase      ,				UpperCAmelCase      ,				UpperCAmelCase          =       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,					task=lowerCAmelCase			)

    UpperCAmelCase          =       model[0].eval()

    recursively_load_weights(lowerCAmelCase			,					lowerCAmelCase			,					not is_finetuned			)

    hf_wavavec.save_pretrained(lowerCAmelCase			)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE_            =    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'''
   )
   SCREAMING_SNAKE_CASE_            =    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 collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

SCREAMING_SNAKE_CASE_            =    datasets.load_iris()

SCREAMING_SNAKE_CASE_            =    np.array(data['''data'''])
SCREAMING_SNAKE_CASE_            =    np.array(data['''target'''])
SCREAMING_SNAKE_CASE_            =    data['''target_names''']

SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_       ,    SCREAMING_SNAKE_CASE_            =    train_test_split(X, y)




def      lowercase__    (       lowerCAmelCase					:       Optional[int]			,					lowerCAmelCase					:       Any			)			->   Optional[Any]:






    """simple docstring"""




    return np.linalg.norm(np.array(lowerCAmelCase			) - np.array(lowerCAmelCase			)			)




def      lowercase__    (       lowerCAmelCase					:       str			,					lowerCAmelCase					:       Any			,					lowerCAmelCase					:       List[Any]			,					lowerCAmelCase					:       Tuple			,					lowerCAmelCase					:       Dict=5			)			->   str:






    """simple docstring"""




    UpperCAmelCase          =       zip(lowerCAmelCase			,					lowerCAmelCase			)
    # List of distances of all points from the point to be classified
    UpperCAmelCase          =       []
    for data_point in data:
        UpperCAmelCase          =       euclidean_distance(data_point[0]			,					lowerCAmelCase			)
        distances.append((distance, data_point[1])			)
    # Choosing 'k' points with the least distances.
    UpperCAmelCase          =       [i[1] for i in sorted(lowerCAmelCase			)[:k]]
    # Most commonly occurring class among them
    # is the class into which the point is classified
    UpperCAmelCase          =       Counter(lowerCAmelCase			).most_common(1			)[0][0]
    return classes[result]


if __name__ == "__main__":
   print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



