






'''simple docstring'''
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
 import PIL

if is_torch_available():
 import torch


UpperCamelCase__			=      logging.get_logger(__name__)


class 							_UpperCAmelCase    (		snake_case      ):
    __lowerCamelCase: Optional[int]									=     ['pixel_values']




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






       '''simple docstring'''






       super().__init__(**a      )
       lowercase_						:     str							     =  size if size is not None else {"shortest_edge": 2_2_4}
       lowercase_						:     List[str]							     =  get_size_dict(a	,  default_to_square=a      )
       lowercase_						:     Optional[Any]							     =  crop_size if crop_size is not None else {"height": 2_5_6, "width": 2_5_6}
       lowercase_						:     Dict							     =  get_size_dict(a	,  param_name="crop_size"      )

       lowercase_						:     Union[str, Any]							     =  do_resize
       lowercase_						:     Optional[int]							     =  size
       lowercase_						:     List[str]							     =  resample
       lowercase_						:     Tuple							     =  do_rescale
       lowercase_						:     Union[str, Any]							     =  rescale_factor
       lowercase_						:     List[str]							     =  do_center_crop
       lowercase_						:     Union[str, Any]							     =  crop_size
       lowercase_						:     Optional[Any]							     =  do_flip_channel_order




    def 					lowerCAmelCase__					(      self	:							int	,  a	:							np.ndarray	,  a	:							Dict[str, int]	,  a	:							PILImageResampling = PIL.Image.BILINEAR	,  a	:							Optional[Union[str, ChannelDimension]] = None	,  **a	:							Optional[Any]	,  ):






       '''simple docstring'''






       lowercase_						:     Any							     =  get_size_dict(a	,  default_to_square=a      )
       if "shortest_edge" not in size:
          raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""      )
       lowercase_						:     Optional[int]							     =  get_resize_output_image_size(a	,  size=size["shortest_edge"]	,  default_to_square=a      )
       return resize(a	,  size=a	,  resample=a	,  data_format=a	,  **a      )




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							np.ndarray	,  a	:							Dict[str, int]	,  a	:							Optional[Union[str, ChannelDimension]] = None	,  **a	:							Optional[int]	,  ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  get_size_dict(a      )
       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 center_crop(a	,  size=(size["height"], size["width"])	,  data_format=a	,  **a      )




    def 					lowerCAmelCase__					(      self	:							str	,  a	:							np.ndarray	,  a	:							Union[int, float]	,  a	:							Optional[Union[str, ChannelDimension]] = None	,  **a	:							Any	,  ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Any	,  a	:							np.ndarray	,  a	:							Optional[Union[str, ChannelDimension]] = None      ):






       '''simple docstring'''






       return flip_channel_order(a	,  data_format=a      )




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






       '''simple docstring'''






       lowercase_						:     List[str]							     =  do_resize if do_resize is not None else self.do_resize
       lowercase_						:     int							     =  resample if resample is not None else self.resample
       lowercase_						:     Dict							     =  do_rescale if do_rescale is not None else self.do_rescale
       lowercase_						:     List[Any]							     =  rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase_						:     Optional[int]							     =  do_center_crop if do_center_crop is not None else self.do_center_crop
       lowercase_						:     Union[str, Any]							     =  (
           do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
       )

       lowercase_						:     Any							     =  size if size is not None else self.size
       lowercase_						:     List[Any]							     =  get_size_dict(a	,  default_to_square=a      )
       lowercase_						:     List[str]							     =  crop_size if crop_size is not None else self.crop_size
       lowercase_						:     int							     =  get_size_dict(a	,  param_name="crop_size"      )

       lowercase_						:     str							     =  make_list_of_images(a      )

       if not valid_images(a      ):
          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."      )

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

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

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

       if do_center_crop:
          lowercase_						:     Union[str, Any]							     =  [self.center_crop(image=a	,  size=a      ) for image in images]

       if do_rescale:
          lowercase_						:     Optional[Any]							     =  [self.rescale(image=a	,  scale=a      ) for image in images]

       # the pretrained checkpoints assume images are BGR, not RGB
       if do_flip_channel_order:
          lowercase_						:     str							     =  [self.flip_channel_order(image=a      ) for image in images]

       lowercase_						:     Optional[int]							     =  [to_channel_dimension_format(a	,  a      ) for image in images]

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





    def 					lowerCAmelCase__					(      self	:							List[str]	,  a	:							List[Any]	,  a	:							List[Tuple] = None      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  outputs.logits

       # Resize logits and compute semantic segmentation maps
       if target_sizes is not None:
          if len(a      ) != len(a      ):
             raise ValueError(
                 "Make sure that you pass in as many target sizes as the batch dimension of the logits"      )

          if is_torch_tensor(a      ):
             lowercase_						:     List[str]							     =  target_sizes.numpy()

          lowercase_						:     Optional[Any]							     =  []

          for idx in range(len(a      )      ):
             lowercase_						:     Tuple							     =  torch.nn.functional.interpolate(
                 logits[idx].unsqueeze(dim=0      )	,  size=target_sizes[idx]	,  mode="bilinear"	,  align_corners=a      )
             lowercase_						:     int							     =  resized_logits[0].argmax(dim=0      )
             semantic_segmentation.append(a      )
       else:
          lowercase_						:     int							     =  logits.argmax(dim=1      )
          lowercase_						:     Tuple							     =  [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]      )]

       return semantic_segmentation












'''simple docstring'''
import argparse

import torch

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


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




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Optional[Any]							     =  WavaVecaForSequenceClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Optional[int]							     =  downstream_dict["projector.weight"]
   lowercase_						:     str							     =  downstream_dict["projector.bias"]
   lowercase_						:     int							     =  downstream_dict["model.post_net.linear.weight"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["model.post_net.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  WavaVecaForAudioFrameClassification.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     Any							     =  downstream_dict["model.linear.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["model.linear.bias"]
   return model




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Any							     =  WavaVecaForXVector.from_pretrained(_UpperCamelCase    ,		config=_UpperCamelCase				)
   lowercase_						:     str							     =  downstream_dict["connector.weight"]
   lowercase_						:     List[str]							     =  downstream_dict["connector.bias"]
   for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
      lowercase_						:     Union[str, Any]							     =  downstream_dict[
          F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
      ]
      lowercase_						:     Dict							     =  downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
   lowercase_						:     List[Any]							     =  downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
   lowercase_						:     Dict							     =  downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
   lowercase_						:     Optional[Any]							     =  downstream_dict["objective.W"]
   return model




@torch.no_grad()
def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  torch.load(_UpperCamelCase    ,		map_location="cpu"				)

   lowercase_						:     Dict							     =  checkpoint["Downstream"]

   lowercase_						:     Optional[Any]							     =  WavaVecaConfig.from_pretrained(_UpperCamelCase				)
   lowercase_						:     Dict							     =  WavaVecaFeatureExtractor.from_pretrained(
       _UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		do_normalize=_UpperCamelCase				)

   lowercase_						:     Dict							     =  hf_config.architectures[0]
   if arch.endswith("ForSequenceClassification"				):
      lowercase_						:     Any							     =  convert_classification(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForAudioFrameClassification"				):
      lowercase_						:     Optional[int]							     =  convert_diarization(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   elif arch.endswith("ForXVector"				):
      lowercase_						:     List[Any]							     =  convert_xvector(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase				)
   else:
      raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

   if hf_config.use_weighted_layer_sum:
      lowercase_						:     List[str]							     =  checkpoint["Featurizer"]["weights"]

   hf_feature_extractor.save_pretrained(_UpperCamelCase				)
   hf_model.save_pretrained(_UpperCamelCase				)


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




