



"""simple docstring"""

import argparse

import torch

from transformers import (
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaForAudioFrameClassification,
    WavaVecaForSequenceClassification,
    WavaVecaForXVector,
    logging,
)


logging.set_verbosity_info()
lowercase__					:						List[Any]												=			logging.get_logger(__name__)




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  List[str]       =			WavaVecaForSequenceClassification.from_pretrained(_a	,    config=_a					)
    snake_case_				:  List[str]       =			downstream_dict['''projector.weight''']
    snake_case_				:  int       =			downstream_dict['''projector.bias''']
    snake_case_				:  Union[str, Any]       =			downstream_dict['''model.post_net.linear.weight''']
    snake_case_				:  int       =			downstream_dict['''model.post_net.linear.bias''']
    return model




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Any       =			WavaVecaForAudioFrameClassification.from_pretrained(_a	,    config=_a					)
    snake_case_				:  Optional[int]       =			downstream_dict['''model.linear.weight''']
    snake_case_				:  Dict       =			downstream_dict['''model.linear.bias''']
    return model




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Optional[Any]       =			WavaVecaForXVector.from_pretrained(_a	,    config=_a					)
    snake_case_				:  List[Any]       =			downstream_dict['''connector.weight''']
    snake_case_				:  Tuple       =			downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel					):
        snake_case_				:  List[Any]       =			downstream_dict[
            f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
        ]
        snake_case_				:  Dict       =			downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]

    snake_case_				:  Tuple       =			downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    snake_case_				:  List[str]       =			downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    snake_case_				:  Tuple       =			downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    snake_case_				:  Optional[int]       =			downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    snake_case_				:  int       =			downstream_dict['''objective.W''']
    return model




@torch.no_grad()
def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    snake_case_				:  Any       =			torch.load(_a	,    map_location='''cpu'''					)

    snake_case_				:  Tuple       =			checkpoint['''Downstream''']

    snake_case_				:  int       =			WavaVecaConfig.from_pretrained(_a					)
    snake_case_				:  List[str]       =			WavaVecaFeatureExtractor.from_pretrained(
        _a	,    return_attention_mask=_a	,    do_normalize=_a					)

    snake_case_				:  Tuple       =			hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''					):
        snake_case_				:  Tuple       =			convert_classification(_a	,    _a	,    _a					)
    elif arch.endswith('''ForAudioFrameClassification'''					):
        snake_case_				:  Optional[int]       =			convert_diarization(_a	,    _a	,    _a					)
    elif arch.endswith('''ForXVector'''					):
        snake_case_				:  Union[str, Any]       =			convert_xvector(_a	,    _a	,    _a					)
    else:
        raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"					)

    if hf_config.use_weighted_layer_sum:
        snake_case_				:  Optional[int]       =			checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(_a					)
    hf_model.save_pretrained(_a					)


if __name__ == "__main__":
     lowercase__					:						Optional[int]												=			argparse.ArgumentParser()
     parser.add_argument(
         '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
     )
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
     parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
     lowercase__					:						Optional[int]												=			parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)










"""simple docstring"""

from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available




@dataclass
class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Union[List[PIL.Image.Image], np.ndarray]
 _lowerCAmelCase     :    Optional[List[bool]]


if is_transformers_available() and is_torch_available():
     from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





