






"""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,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_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


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





class   UpperCamelCase_ (       a_  ):
  _A       :					List[str]				=	['pixel_values']



  def __init__(      self ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 0.9 ,		snake_case__ = PILImageResampling.BICUBIC ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = 1 / 2_55 ,		snake_case__ = True ,		snake_case__ = True ,		snake_case__ = None ,		snake_case__ = None ,		**snake_case__ ,		)			->			None:



         """simple docstring"""





         super().__init__(**snake_case__				)
         UpperCAmelCase	    =			size if size is not None else {"""shortest_edge""": 2_24}
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         UpperCAmelCase	    =			crop_size if crop_size is not None else {"""height""": 2_24, """width""": 2_24}
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		param_name="""crop_size"""				)

         UpperCAmelCase	    =			do_resize
         UpperCAmelCase	    =			size
         UpperCAmelCase	    =			crop_pct
         UpperCAmelCase	    =			resample
         UpperCAmelCase	    =			do_center_crop
         UpperCAmelCase	    =			crop_size
         UpperCAmelCase	    =			do_rescale
         UpperCAmelCase	    =			rescale_factor
         UpperCAmelCase	    =			do_normalize
         UpperCAmelCase	    =			image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
         UpperCAmelCase	    =			image_std if image_std is not None else IMAGENET_DEFAULT_STD



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = PILImageResampling.BICUBIC ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         if "shortest_edge" not in size and ("height" not in size or "width" not in size):
                raise ValueError(f'''size must contain \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}'''				)
         if crop_pct is not None:
                if "shortest_edge" in size:
                       UpperCAmelCase	    =			int(size["""shortest_edge"""] / crop_pct				)
                elif "height" in size and "width" in size:
                       if size["height"] == size["width"]:
                              UpperCAmelCase	    =			int(size["""height"""] / crop_pct				)
                       else:
                              UpperCAmelCase	    =			(int(size["""height"""] / crop_pct				), int(size["""width"""] / crop_pct				))
                else:
                       raise ValueError("""Invalid size for resize: {}""".format(snake_case__				)				)

                UpperCAmelCase	    =			get_resize_output_image_size(snake_case__ ,		size=snake_case__ ,		default_to_square=snake_case__				)
         else:
                if "shortest_edge" in size:
                       UpperCAmelCase	    =			get_resize_output_image_size(snake_case__ ,		size=size["""shortest_edge"""] ,		default_to_square=snake_case__				)
                elif "height" in size and "width" in size:
                       UpperCAmelCase	    =			(size["""height"""], size["""width"""])
                else:
                       raise ValueError("""Invalid size for resize: {}""".format(snake_case__				)				)

         return resize(snake_case__ ,		size=snake_case__ ,		resample=snake_case__ ,		data_format=snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





         UpperCAmelCase	    =			get_size_dict(snake_case__				)
         if "height" not in size or "width" not in size:
                raise ValueError(f'''size must contain \'height\' and \'width\' as keys. Got {size.keys()}'''				)
         return center_crop(snake_case__ ,		size=(size["""height"""], size["""width"""]) ,		data_format=snake_case__ ,		**snake_case__				)



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			Tuple:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ ,		snake_case__ ,		snake_case__ = None ,		**snake_case__ ,		)			->			np.ndarray:



         """simple docstring"""





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



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = None ,		snake_case__ = ChannelDimension.FIRST ,		**snake_case__ ,		)			->			PIL.Image.Image:



         """simple docstring"""





         UpperCAmelCase	    =			do_resize if do_resize is not None else self.do_resize
         UpperCAmelCase	    =			crop_pct if crop_pct is not None else self.crop_pct
         UpperCAmelCase	    =			resample if resample is not None else self.resample
         UpperCAmelCase	    =			do_center_crop if do_center_crop is not None else self.do_center_crop
         UpperCAmelCase	    =			do_rescale if do_rescale is not None else self.do_rescale
         UpperCAmelCase	    =			rescale_factor if rescale_factor is not None else self.rescale_factor
         UpperCAmelCase	    =			do_normalize if do_normalize is not None else self.do_normalize
         UpperCAmelCase	    =			image_mean if image_mean is not None else self.image_mean
         UpperCAmelCase	    =			image_std if image_std is not None else self.image_std

         UpperCAmelCase	    =			size if size is not None else self.size
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		default_to_square=snake_case__				)
         UpperCAmelCase	    =			crop_size if crop_size is not None else self.crop_size
         UpperCAmelCase	    =			get_size_dict(snake_case__ ,		param_name="""crop_size"""				)

         UpperCAmelCase	    =			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 or resample is None:
                raise ValueError("""Size and resample must be specified if do_resize is True."""				)

         if do_center_crop and crop_pct is None:
                raise ValueError("""Crop_pct 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	    =			[to_numpy_array(snake_case__				) for image in images]

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

         if do_center_crop:
                UpperCAmelCase	    =			[self.center_crop(image=snake_case__ ,		size=snake_case__				) for image in images]

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

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

         UpperCAmelCase	    =			[to_channel_dimension_format(snake_case__ ,		snake_case__				) for image in images]

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








"""simple docstring"""







import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase_	:    Any            =      logging.get_logger(__name__)






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""projector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""projector.bias"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.post_net.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""model.linear.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.linear.bias"""]
       return model






def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			UniSpeechSatForXVector.from_pretrained(lowerCAmelCase	,      config=lowerCAmelCase		)
       UpperCAmelCase	    =			downstream_dict["""connector.weight"""]
       UpperCAmelCase	    =			downstream_dict["""connector.bias"""]
       for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
              UpperCAmelCase	    =			downstream_dict[
                  F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
              ]
              UpperCAmelCase	    =			downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
       UpperCAmelCase	    =			downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
       UpperCAmelCase	    =			downstream_dict["""objective.W"""]
       return model






@torch.no_grad()
def       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			torch.load(lowerCAmelCase	,      map_location="""cpu"""		)

       UpperCAmelCase	    =			checkpoint["""Downstream"""]

       UpperCAmelCase	    =			UniSpeechSatConfig.from_pretrained(lowerCAmelCase		)
       UpperCAmelCase	    =			WavaVecaFeatureExtractor.from_pretrained(
           lowerCAmelCase	,      return_attention_mask=lowerCAmelCase	,      do_normalize=lowerCAmelCase		)

       UpperCAmelCase	    =			hf_config.architectures[0]
       if arch.endswith("""ForSequenceClassification"""		):
              UpperCAmelCase	    =			convert_classification(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       elif arch.endswith("""ForAudioFrameClassification"""		):
              UpperCAmelCase	    =			convert_diarization(lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		)
       elif arch.endswith("""ForXVector"""		):
              UpperCAmelCase	    =			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	    =			checkpoint["""Featurizer"""]["""weights"""]

       hf_feature_extractor.save_pretrained(lowerCAmelCase		)
       hf_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
 lowerCAmelCase_	:    List[Any]            =      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.''')
 lowerCAmelCase_	:    str            =      parser.parse_args()
 convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
