
import argparse

import torch

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


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






def 						__magic_name__			(   __a      :	str	,		__a      :	int	,		__a      :	List[Any]						):



					'''simple docstring'''




					UpperCamelCase__				=    WavaVecaForSequenceClassification.from_pretrained(__a	,		config=__a						)
					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 						__magic_name__			(   __a      :	Tuple	,		__a      :	Any	,		__a      :	List[str]						):



					'''simple docstring'''




					UpperCamelCase__				=    WavaVecaForAudioFrameClassification.from_pretrained(__a	,		config=__a						)
					UpperCamelCase__				=    downstream_dict["""model.linear.weight"""]
					UpperCamelCase__				=    downstream_dict["""model.linear.bias"""]
					return model






def 						__magic_name__			(   __a      :	Dict	,		__a      :	Union[str, Any]	,		__a      :	List[str]						):



					'''simple docstring'''




					UpperCamelCase__				=    WavaVecaForXVector.from_pretrained(__a	,		config=__a						)
					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 						__magic_name__			(   __a      :	Optional[int]	,		__a      :	Optional[int]	,		__a      :	Optional[int]	,		__a      :	Any						):



					'''simple docstring'''




					UpperCamelCase__				=    torch.load(__a	,		map_location="""cpu"""						)

					UpperCamelCase__				=    checkpoint["""Downstream"""]

					UpperCamelCase__				=    WavaVecaConfig.from_pretrained(__a						)
					UpperCamelCase__				=    WavaVecaFeatureExtractor.from_pretrained(
					    __a	,		return_attention_mask=__a	,		do_normalize=__a						)

					UpperCamelCase__				=    hf_config.architectures[0]
					if arch.endswith("""ForSequenceClassification"""						):
										UpperCamelCase__				=    convert_classification(__a	,		__a	,		__a						)
					elif arch.endswith("""ForAudioFrameClassification"""						):
										UpperCamelCase__				=    convert_diarization(__a	,		__a	,		__a						)
					elif arch.endswith("""ForXVector"""						):
										UpperCamelCase__				=    convert_xvector(__a	,		__a	,		__a						)
					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(__a						)
					hf_model.save_pretrained(__a						)


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







import argparse
from collections import defaultdict

import yaml


lowerCamelCase_						     =					'''docs/source/en/_toctree.yml'''






def 						__magic_name__			(   __a      :	Union[str, Any]						):



     '''simple docstring'''




     UpperCamelCase__				=    defaultdict(__a						)
     for doc in model_doc:
          counts[doc["local"]] += 1
     UpperCamelCase__				=    [key for key, value in counts.items() if value > 1]

     UpperCamelCase__				=    []
     for duplicate_key in duplicates:
          UpperCamelCase__				=    list({doc["""title"""] for doc in model_doc if doc["""local"""] == duplicate_key}						)
          if len(__a						) > 1:
               raise ValueError(
                   f"{duplicate_key} is present several times in the documentation table of content at "
                   """`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the """
                   """others."""						)
          # Only add this once
          new_doc.append({"""local""": duplicate_key, """title""": titles[0]}						)

     # Add none duplicate-keys
     new_doc.extend([doc for doc in model_doc if counts[doc["""local"""]] == 1]						)

     # Sort
     return sorted(__a	,		key=lambda __a						: s["title"].lower()						)






def 						__magic_name__			(   __a      :	int=False						):



     '''simple docstring'''




     with open(__a	,		encoding="""utf-8"""						) as f:
          UpperCamelCase__				=    yaml.safe_load(f.read()						)

     # Get to the API doc
     UpperCamelCase__				=    0
     while content[api_idx]["title"] != "API":
          api_idx += 1
     UpperCamelCase__				=    content[api_idx]["""sections"""]

     # Then to the model doc
     UpperCamelCase__				=    0
     while api_doc[model_idx]["title"] != "Models":
          model_idx += 1

     UpperCamelCase__				=    api_doc[model_idx]["""sections"""]

     UpperCamelCase__				=    [(idx, section) for idx, section in enumerate(__a						) if """sections""" in section]
     UpperCamelCase__				=    False
     for idx, modality_doc in modalities_docs:
          UpperCamelCase__				=    modality_doc["""sections"""]
          UpperCamelCase__				=    clean_model_doc_toc(__a						)

          if old_modality_doc != new_modality_doc:
               UpperCamelCase__				=    True
               if overwrite:
                    UpperCamelCase__				=    new_modality_doc

     if diff:
          if overwrite:
               UpperCamelCase__				=    model_doc
               UpperCamelCase__				=    api_doc
               with open(__a	,		"""w"""	,		encoding="""utf-8"""						) as f:
                    f.write(yaml.dump(__a	,		allow_unicode=__a						)						)
          else:
               raise ValueError(
                   """The model doc part of the table of content is not properly sorted, run `make style` to fix this."""						)


if __name__ == "__main__":
    lowerCamelCase_						     =					argparse.ArgumentParser()
    parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
    lowerCamelCase_						     =					parser.parse_args()

    check_model_doc(args.fix_and_overwrite)





