
"""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 center_crop, 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, is_vision_available, logging


if is_vision_available():
    import PIL


_a									=				logging.get_logger(__name__)


class A_      (lowercase__ ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		int    	=						["""pixel_values"""]







  def __init__(						self							,  lowercase_ = True							,  lowercase_ = None							,  lowercase_ = PIL.Image.NEAREST							,  lowercase_ = False							,  lowercase_ = None							,  lowercase_ = 1 / 255							,  lowercase_ = False							,  lowercase_ = True							,  lowercase_ = True							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = True							,  **lowercase_							,  ):

     """simple docstring"""
     super().__init__(**lowercase_    )
     UpperCAmelCase_   :   List[str]     	=       size if size is not None else {"height": 346, "width": 346}
     UpperCAmelCase_   :   Optional[int]     	=       get_size_dict(lowercase_    )
     UpperCAmelCase_   :   str     	=       crop_size if crop_size is not None else {"height": 289, "width": 289}
     UpperCAmelCase_   :   str     	=       get_size_dict(lowercase_							,  param_name="crop_size"    )

     UpperCAmelCase_   :   Any     	=       do_resize
     UpperCAmelCase_   :   Tuple     	=       size
     UpperCAmelCase_   :   Union[str, Any]     	=       resample
     UpperCAmelCase_   :   Dict     	=       do_center_crop
     UpperCAmelCase_   :   Optional[Any]     	=       crop_size
     UpperCAmelCase_   :   Dict     	=       do_rescale
     UpperCAmelCase_   :   Dict     	=       rescale_factor
     UpperCAmelCase_   :   List[str]     	=       rescale_offset
     UpperCAmelCase_   :   Optional[int]     	=       do_normalize
     UpperCAmelCase_   :   Optional[Any]     	=       image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     UpperCAmelCase_   :   Union[str, Any]     	=       image_std if image_std is not None else IMAGENET_STANDARD_STD
     UpperCAmelCase_   :   str     	=       include_top







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_ = PIL.Image.NEAREST							,  lowercase_ = None							,  **lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       get_size_dict(lowercase_    )
     if "height" not in size or "width" not in size:
        raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
     return resize(
         lowercase_							,  size=(size["height"], size["width"])							,  resample=lowercase_							,  data_format=lowercase_							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_ = None							,  **lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       get_size_dict(lowercase_    )
     if "height" not in size or "width" not in size:
        raise ValueError(F"""The size dictionary must have keys 'height' and 'width'. Got {size.keys()}"""    )
     return center_crop(lowercase_							,  size=(size["height"], size["width"])							,  data_format=lowercase_							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_ = True							,  lowercase_ = None							,  **lowercase_							,  ):

     """simple docstring"""
     if offset:
        UpperCAmelCase_   :   Optional[int]     	=       (image - 1_27.5) * scale
        if data_format is not None:
           UpperCAmelCase_   :   int     	=       to_channel_dimension_format(lowercase_							,  lowercase_    )
        UpperCAmelCase_   :   Optional[int]     	=       rescaled_image.astype(np.floataa    )
     else:
        UpperCAmelCase_   :   List[str]     	=       rescale(lowercase_							,  scale=lowercase_							,  data_format=lowercase_							,  **lowercase_    )
     return rescaled_image







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_							,  lowercase_ = None							,  **lowercase_							,  ):

     """simple docstring"""
     return normalize(lowercase_							,  mean=lowercase_							,  std=lowercase_							,  data_format=lowercase_							,  **lowercase_    )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_=None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = None							,  lowercase_ = ChannelDimension.FIRST							,  **lowercase_							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       do_resize if do_resize is not None else self.do_resize
     UpperCAmelCase_   :   Optional[int]     	=       resample if resample is not None else self.resample
     UpperCAmelCase_   :   int     	=       do_center_crop if do_center_crop is not None else self.do_center_crop
     UpperCAmelCase_   :   Tuple     	=       do_rescale if do_rescale is not None else self.do_rescale
     UpperCAmelCase_   :   List[str]     	=       rescale_factor if rescale_factor is not None else self.rescale_factor
     UpperCAmelCase_   :   str     	=       rescale_offset if rescale_offset is not None else self.rescale_offset
     UpperCAmelCase_   :   str     	=       do_normalize if do_normalize is not None else self.do_normalize
     UpperCAmelCase_   :   List[str]     	=       image_mean if image_mean is not None else self.image_mean
     UpperCAmelCase_   :   Any     	=       image_std if image_std is not None else self.image_std
     UpperCAmelCase_   :   List[Any]     	=       include_top if include_top is not None else self.include_top

     UpperCAmelCase_   :   List[str]     	=       size if size is not None else self.size
     UpperCAmelCase_   :   Optional[int]     	=       get_size_dict(lowercase_    )
     UpperCAmelCase_   :   int     	=       crop_size if crop_size is not None else self.crop_size
     UpperCAmelCase_   :   Tuple     	=       get_size_dict(lowercase_							,  param_name="crop_size"    )

     UpperCAmelCase_   :   List[Any]     	=       make_list_of_images(lowercase_    )

     if not valid_images(lowercase_    ):
        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 or resample is None:
        raise ValueError("Size and resample must be specified if do_resize is True."    )

     if do_center_crop and crop_size is None:
        raise ValueError("Crop size must be specified if do_center_crop is True."    )

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

     if do_normalize and (image_mean is None or image_std is None):
        raise ValueError("Image mean and std must be specified if do_normalize is True."    )

     # All transformations expect numpy arrays.
     UpperCAmelCase_   :   Any     	=       [to_numpy_array(lowercase_    ) for image in images]

     if do_resize:
        UpperCAmelCase_   :   str     	=       [self.resize(image=lowercase_							,  size=lowercase_							,  resample=lowercase_    ) for image in images]

     if do_center_crop:
        UpperCAmelCase_   :   Dict     	=       [self.center_crop(image=lowercase_							,  size=lowercase_    ) for image in images]

     if do_rescale:
        UpperCAmelCase_   :   Union[str, Any]     	=       [self.rescale(image=lowercase_							,  scale=lowercase_							,  offset=lowercase_    ) for image in images]

     if do_normalize:
        UpperCAmelCase_   :   Optional[Any]     	=       [self.normalize(image=lowercase_							,  mean=lowercase_							,  std=lowercase_    ) for image in images]

     if include_top:
        UpperCAmelCase_   :   Optional[int]     	=       [self.normalize(image=lowercase_							,  mean=[0, 0, 0]							,  std=lowercase_    ) for image in images]

     UpperCAmelCase_   :   str     	=       [to_channel_dimension_format(lowercase_							,  lowercase_    ) for image in images]

     UpperCAmelCase_   :   List[Any]     	=       {"pixel_values": images}
     return BatchFeature(data=lowercase_							,  tensor_type=lowercase_    )




"""simple docstring"""





import argparse

import torch

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


logging.set_verbosity_info()
_a									=				logging.get_logger(__name__)







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   str     	=       WavLMForSequenceClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.weight"]
   UpperCAmelCase_   :   int     	=       downstream_dict["projector.bias"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.weight"]
   UpperCAmelCase_   :   Optional[Any]     	=       downstream_dict["model.post_net.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       WavLMForAudioFrameClassification.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.linear.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.linear.bias"]
   return model







def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Tuple     	=       WavLMForXVector.from_pretrained(__lowerCamelCase,						config=__lowerCamelCase    )
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["connector.weight"]
   UpperCAmelCase_   :   str     	=       downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel    ):
      UpperCAmelCase_   :   List[str]     	=       downstream_dict[
          f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   UpperCAmelCase_   :   Dict     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   UpperCAmelCase_   :   List[Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   UpperCAmelCase_   :   List[str]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   UpperCAmelCase_   :   Union[str, Any]     	=       downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   UpperCAmelCase_   :   Dict     	=       downstream_dict["objective.W"]
   return model







@torch.no_grad()
def       __a	(						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    ):
   UpperCAmelCase_   :   Optional[Any]     	=       torch.load(__lowerCamelCase,						map_location="cpu"    )

   UpperCAmelCase_   :   Union[str, Any]     	=       checkpoint["Downstream"]

   UpperCAmelCase_   :   List[str]     	=       WavLMConfig.from_pretrained(__lowerCamelCase    )
   UpperCAmelCase_   :   List[str]     	=       WavaVecaFeatureExtractor.from_pretrained(
       __lowerCamelCase,						return_attention_mask=__lowerCamelCase,						do_normalize=__lowerCamelCase    )

   UpperCAmelCase_   :   Any     	=       hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"    ):
      UpperCAmelCase_   :   Optional[int]     	=       convert_classification(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForAudioFrameClassification"    ):
      UpperCAmelCase_   :   Dict     	=       convert_diarization(__lowerCamelCase,						__lowerCamelCase,						__lowerCamelCase    )
   elif arch.endswith("ForXVector"    ):
      UpperCAmelCase_   :   List[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:
      UpperCAmelCase_   :   Optional[int]     	=       checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(__lowerCamelCase    )
   hf_model.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
    _a									=				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.')
    _a									=				parser.parse_args()
    convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)


