





"""simple docstring"""

import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize




def __lowercase    (							_a					):
    return np.dot(_a	,    _a					)




class  _UpperCAmelCase :

 def __init__(  self	: int						,  *,
     lowercase_	: float = np.inf						,  lowercase_	: str = "linear"						,  lowercase_	: float = 0.0						,  ):
     snake_case_				:  Optional[Any]       =			regularization
     snake_case_				:  Tuple       =			gamma
     if kernel == "linear":
         snake_case_				:  int       =			self.__linear
     elif kernel == "rbf":
         if self.gamma == 0:
             raise ValueError('''rbf kernel requires gamma'''       )
         if not isinstance(self.gamma						,  (float, int)       ):
             raise ValueError('''gamma must be float or int'''       )
         if not self.gamma > 0:
             raise ValueError('''gamma must be > 0'''       )
         snake_case_				:  int       =			self.__rbf
         # in the future, there could be a default value like in sklearn
         # sklear: def_gamma = 1/(n_features * X.var()) (wiki)
         # previously it was 1/(n_features)
     else:
         snake_case_				:  List[Any]       =			f"Unknown kernel: {kernel}"
         raise ValueError(lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.dot(lowercase_						,  lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: ndarray						,  lowercase_	: ndarray       ):
     return np.exp(-(self.gamma * norm_squared(vectora - vectora       ))       )

 def 		_snake_case      (  self	: Any						,  lowercase_	: list[ndarray]						,  lowercase_	: ndarray       ):
     snake_case_				:  Union[str, Any]       =			observations
     snake_case_				:  int       =			classes

     # using Wolfe's Dual to calculate w.
     # Primal problem: minimize 1/2*norm_squared(w)
     #   constraint: yn(w . xn + b) >= 1
     #
     # With l a vector
     # Dual problem: maximize sum_n(ln) -
     #       1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
     #   constraint: self.C >= ln >= 0
     #           and sum_n(ln*yn) = 0
     # Then we get w using w = sum_n(ln*yn*xn)
     # At the end we can get b ~= mean(yn - w . xn)
     #
     # Since we use kernels, we only need l_star to calculate b
     # and to classify observations

     (snake_case_			)				:  List[str]       =			np.shape(lowercase_       )

     def to_minimize(lowercase_	: ndarray       ) -> float:
         snake_case_				:  Tuple       =			0
         (snake_case_			)				:  Optional[Any]       =			np.shape(lowercase_       )
         for i in range(lowercase_       ):
             for j in range(lowercase_       ):
                 s += (
                     candidate[i]
                     * candidate[j]
                     * classes[i]
                     * classes[j]
                     * self.kernel(observations[i]						,  observations[j]       )
                 )
         return 1 / 2 * s - sum(lowercase_       )

     snake_case_				:  Optional[Any]       =			LinearConstraint(lowercase_						,  0						,  0       )
     snake_case_				:  str       =			Bounds(0						,  self.regularization       )

     snake_case_				:  int       =			minimize(
         lowercase_						,  np.ones(lowercase_       )						,  bounds=lowercase_						,  constraints=[ly_contraint]       ).x
     snake_case_				:  Optional[Any]       =			l_star

     # calculating mean offset of separation plane to points
     snake_case_				:  List[Any]       =			0
     for i in range(lowercase_       ):
         for j in range(lowercase_       ):
             s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
                 observations[i]						,  observations[j]       )
     snake_case_				:  Union[str, Any]       =			s / n






 def 		_snake_case      (  self	: List[str]						,  lowercase_	: ndarray       ):
     snake_case_				:  int       =			sum(
         self.optimum[n]
         * self.classes[n]
         * self.kernel(self.observations[n]						,  lowercase_       )
         for n in range(len(self.classes       )       )       )
     return 1 if s + self.offset >= 0 else -1


if __name__ == "__main__":
     import doctest

     doctest.testmod()










"""simple docstring"""

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
lowercase__					:						Dict												=			logging.get_logger(__name__)

lowercase__					:						List[str]												=			{
    '''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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''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''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
lowercase__					:						Dict												=			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    for attribute in key.split('''.'''					):
        snake_case_				:  Optional[Any]       =			getattr(_a	,    _a					)

    if weight_type is not None:
        snake_case_				:  Optional[int]       =			getattr(_a	,    _a					).shape
    else:
        snake_case_				:  Optional[int]       =			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_				:  Tuple       =			value
    elif weight_type == "weight_v":
        snake_case_				:  Tuple       =			value
    elif weight_type == "bias":
        snake_case_				:  int       =			value
    else:
        snake_case_				:  Optional[Any]       =			value

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




def __lowercase    (							_a	,    _a					):
    snake_case_				:  Optional[int]       =			[]
    snake_case_				:  Optional[Any]       =			fairseq_model.state_dict()

    snake_case_				:  Optional[int]       =			hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case_				:  int       =			False
        if "conv_layers" in name:
            load_conv_layer(
                _a	,    _a	,    _a	,    _a	,    hf_model.config.feat_extract_norm == '''group'''	,    )
            snake_case_				:  Any       =			True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case_				:  Tuple       =			'''unispeech_sat.''' + 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]:
                    if "layer_norm_for_extract" in name and (".".join(name.split('''.'''					)[:-1]					) != key):
                        # special case since naming is very similar
                        continue
                    snake_case_				:  List[str]       =			True
                    if "*" in mapped_key:
                        snake_case_				:  List[Any]       =			name.split(_a					)[0].split('''.'''					)[-2]
                        snake_case_				:  Dict       =			mapped_key.replace('''*'''	,    _a					)
                    if "weight_g" in name:
                        snake_case_				:  Any       =			'''weight_g'''
                    elif "weight_v" in name:
                        snake_case_				:  List[str]       =			'''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[int]       =			'''weight'''
                    else:
                        snake_case_				:  List[Any]       =			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 __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			full_name.split('''conv_layers.'''					)[-1]
    snake_case_				:  int       =			name.split('''.'''					)
    snake_case_				:  int       =			int(items[0]					)
    snake_case_				:  int       =			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_				:  int       =			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_				:  str       =			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[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[layer_id].layer_norm.weight.data.shape} was found."					)
            snake_case_				:  Optional[Any]       =			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 __lowercase    (							_a	,    _a	,    _a=None	,    _a=None	,    _a=True					):
    if config_path is not None:
        snake_case_				:  int       =			UniSpeechSatConfig.from_pretrained(_a					)
    else:
        snake_case_				:  Tuple       =			UniSpeechSatConfig()

    snake_case_				:  List[Any]       =			''''''

    if is_finetuned:
        snake_case_				:  str       =			UniSpeechSatForCTC(_a					)
    else:
        snake_case_				:  Dict       =			UniSpeechSatForPreTraining(_a					)

    snake_case_,			snake_case_,			snake_case_				:  str       =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]	,    arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
    snake_case_				:  Dict       =			model[0].eval()

    recursively_load_weights(_a	,    _a					)

    hf_wavavec.save_pretrained(_a					)


if __name__ == "__main__":
     lowercase__					:						List[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'''
     )
     lowercase__					:						Optional[int]												=			parser.parse_args()
     convert_unispeech_sat_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )





