
"""simple docstring"""
import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
lowercase		:						Union[str, Any] =					logging.get_logger(__name__)


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[int]:


     '''simple docstring'''
     __a:						Tuple		    =					WavLMForSequenceClassification.from_pretrained(lowerCamelCase_	,				config=lowerCamelCase_ )
     __a:						Any		    =					downstream_dict["""projector.weight"""]
     __a:						List[str]		    =					downstream_dict["""projector.bias"""]
     __a:						int		    =					downstream_dict["""model.post_net.linear.weight"""]
     __a:						Union[str, Any]		    =					downstream_dict["""model.post_net.linear.bias"""]
     return model


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[int]:


     '''simple docstring'''
     __a:						str		    =					WavLMForAudioFrameClassification.from_pretrained(lowerCamelCase_	,				config=lowerCamelCase_ )
     __a:						Optional[Any]		    =					downstream_dict["""model.linear.weight"""]
     __a:						Any		    =					downstream_dict["""model.linear.bias"""]
     return model


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[int]:


     '''simple docstring'''
     __a:						Tuple		    =					WavLMForXVector.from_pretrained(lowerCamelCase_	,				config=lowerCamelCase_ )
     __a:						int		    =					downstream_dict["""connector.weight"""]
     __a:						Optional[int]		    =					downstream_dict["""connector.bias"""]
     for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
          __a:						Tuple		    =					downstream_dict[
              f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
          ]
          __a:						Dict		    =					downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

     __a:						List[Any]		    =					downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
     __a:						Optional[int]		    =					downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
     __a:						Tuple		    =					downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
     __a:						Union[str, Any]		    =					downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
     __a:						Union[str, Any]		    =					downstream_dict["""objective.W"""]
     return model


@torch.no_grad()
def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_ )     ->			Optional[Any]:


     '''simple docstring'''
     __a:						Optional[int]		    =					torch.load(lowerCamelCase_	,				map_location="""cpu""" )

     __a:						Optional[Any]		    =					checkpoint["""Downstream"""]

     __a:						Dict		    =					WavLMConfig.from_pretrained(lowerCamelCase_ )
     __a:						int		    =					WavaVecaFeatureExtractor.from_pretrained(
         lowerCamelCase_	,				return_attention_mask=lowerCamelCase_	,				do_normalize=lowerCamelCase_ )

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

     if hf_config.use_weighted_layer_sum:
          __a:						Optional[Any]		    =					checkpoint["""Featurizer"""]["""weights"""]

     hf_feature_extractor.save_pretrained(lowerCamelCase_ )
     hf_model.save_pretrained(lowerCamelCase_ )


if __name__ == "__main__":
   lowercase		:						str =					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		:						Any =					parser.parse_args()
   convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


"""simple docstring"""
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase		:						Tuple =					logging.get_logger(__name__)




class    _snake_case  (						_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						['''pixel_values''']






							def __init__(					self						:			Union[str, Any]						, snake_case__						:			bool = True						, snake_case__						:			Optional[Dict[str, int]] = None						, snake_case__						:			PILImageResampling = PILImageResampling.BILINEAR						, snake_case__						:			bool = True						, snake_case__						:			Union[int, float] = 1 / 2_5_5						, snake_case__						:			bool = True						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, **snake_case__						:			Optional[int]						, ):






												"""simple docstring"""



												super().__init__(**snake_case__    )
												__a:						Dict		    =					size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
												__a:						Optional[Any]		    =					get_size_dict(snake_case__    )
												__a:						Union[str, Any]		    =					do_resize
												__a:						str		    =					do_rescale
												__a:						Tuple		    =					do_normalize
												__a:						Dict		    =					size
												__a:						str		    =					resample
												__a:						Any		    =					rescale_factor
												__a:						Optional[int]		    =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
												__a:						int		    =					image_std if image_std is not None else IMAGENET_STANDARD_STD






							def 							__lowercase  (					self						:			Optional[Any]						, snake_case__						:			np.ndarray						, snake_case__						:			Dict[str, int]						, snake_case__						:			PILImageResampling = PILImageResampling.BILINEAR						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			List[str]						, ):






												"""simple docstring"""



												__a:						int		    =					get_size_dict(snake_case__    )
												if "height" not in size or "width" not in size:
																	raise ValueError(f"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""    )
												return resize(
												    snake_case__						, size=(size["""height"""], size["""width"""])						, resample=snake_case__						, data_format=snake_case__						, **snake_case__    )






							def 							__lowercase  (					self						:			List[str]						, snake_case__						:			np.ndarray						, snake_case__						:			float						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			Union[str, Any]    ):






												"""simple docstring"""



												return rescale(snake_case__						, scale=snake_case__						, data_format=snake_case__						, **snake_case__    )






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			np.ndarray						, snake_case__						:			Union[float, List[float]]						, snake_case__						:			Union[float, List[float]]						, snake_case__						:			Optional[Union[str, ChannelDimension]] = None						, **snake_case__						:			Union[str, Any]						, ):






												"""simple docstring"""



												return normalize(snake_case__						, mean=snake_case__						, std=snake_case__						, data_format=snake_case__						, **snake_case__    )






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			ImageInput						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Dict[str, int] = None						, snake_case__						:			PILImageResampling = None						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Optional[float] = None						, snake_case__						:			Optional[bool] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[float, List[float]]] = None						, snake_case__						:			Optional[Union[str, TensorType]] = None						, snake_case__						:			Union[str, ChannelDimension] = ChannelDimension.FIRST						, **snake_case__						:			List[str]						, ):






												"""simple docstring"""



												__a:						Any		    =					do_resize if do_resize is not None else self.do_resize
												__a:						Any		    =					do_rescale if do_rescale is not None else self.do_rescale
												__a:						int		    =					do_normalize if do_normalize is not None else self.do_normalize
												__a:						Any		    =					resample if resample is not None else self.resample
												__a:						Any		    =					rescale_factor if rescale_factor is not None else self.rescale_factor
												__a:						Dict		    =					image_mean if image_mean is not None else self.image_mean
												__a:						List[str]		    =					image_std if image_std is not None else self.image_std

												__a:						int		    =					size if size is not None else self.size
												__a:						Any		    =					get_size_dict(snake_case__    )

												__a:						List[str]		    =					make_list_of_images(snake_case__    )

												if not valid_images(snake_case__    ):
																	raise ValueError(
																	    """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
																	    """torch.Tensor, tf.Tensor or jax.ndarray."""    )

												if do_resize and size is None:
																	raise ValueError("""Size must be specified if do_resize is True."""    )

												if do_rescale and rescale_factor is None:
																	raise ValueError("""Rescale factor must be specified if do_rescale is True."""    )

												# All transformations expect numpy arrays.
												__a:						str		    =					[to_numpy_array(snake_case__    ) for image in images]

												if do_resize:
																	__a:						Dict		    =					[self.resize(image=snake_case__						, size=snake_case__						, resample=snake_case__    ) for image in images]

												if do_rescale:
																	__a:						str		    =					[self.rescale(image=snake_case__						, scale=snake_case__    ) for image in images]

												if do_normalize:
																	__a:						Dict		    =					[self.normalize(image=snake_case__						, mean=snake_case__						, std=snake_case__    ) for image in images]

												__a:						int		    =					[to_channel_dimension_format(snake_case__						, snake_case__    ) for image in images]

												__a:						str		    =					{"""pixel_values""": images}
												return BatchFeature(data=snake_case__						, tensor_type=snake_case__    )
