






"""simple docstring"""







from __future__ import annotations

from collections import deque





class   UpperCamelCase_ :



  def __init__(      self ,		snake_case__				)			->			List[Any]:



         """simple docstring"""





         lowercase_	    =			[]
         self.adlist.append(
             {"""value""": """""", """next_states""": [], """fail_state""": 0, """output""": []}				)

         for keyword in keywords:
                self.add_keyword(UpperCAmelCase_				)
         self.set_fail_transitions()



  def 		UpperCamelCase_				(      self ,		snake_case__ ,		snake_case__				)			->			int | None:



         """simple docstring"""





         for state in self.adlist[current_state]["next_states"]:
                if char == self.adlist[state]["value"]:
                       return state
         return None



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			None:



         """simple docstring"""





         lowercase_	    =			0
         for character in keyword:
                lowercase_	    =			self.find_next_state(UpperCAmelCase_ ,		UpperCAmelCase_				)
                if next_state is None:
                       self.adlist.append(
                           {
                               """value""": character,
                               """next_states""": [],
                               """fail_state""": 0,
                               """output""": [],
                           }				)
                       self.adlist[current_state]["next_states"].append(len(self.adlist				) - 1				)
                       lowercase_	    =			len(self.adlist				) - 1
                else:
                       lowercase_	    =			next_state
         self.adlist[current_state]["output"].append(UpperCAmelCase_				)



  def 		UpperCamelCase_				(      self				)			->			None:



         """simple docstring"""





         lowercase_	    =			deque()
         for node in self.adlist[0]["next_states"]:
                q.append(UpperCAmelCase_				)
                lowercase_	    =			0
         while q:
                lowercase_	    =			q.popleft()
                for child in self.adlist[r]["next_states"]:
                       q.append(UpperCAmelCase_				)
                       lowercase_	    =			self.adlist[r]["""fail_state"""]
                       while (
                           self.find_next_state(UpperCAmelCase_ ,		self.adlist[child]["""value"""]				) is None
                           and state != 0
                       ):
                              lowercase_	    =			self.adlist[state]["""fail_state"""]
                       lowercase_	    =			self.find_next_state(
                           UpperCAmelCase_ ,		self.adlist[child]["""value"""]				)
                       if self.adlist[child]["fail_state"] is None:
                              lowercase_	    =			0
                       lowercase_	    =			(
                           self.adlist[child]["""output"""]
                           + self.adlist[self.adlist[child]["""fail_state"""]]["""output"""]
                       )



  def 		UpperCamelCase_				(      self ,		snake_case__				)			->			dict[str, list[int]]:



         """simple docstring"""





         lowercase_	    =			{}  # returns a dict with keywords and list of its occurrences
         lowercase_	    =			0
         for i in range(len(UpperCAmelCase_				)				):
                while (
                    self.find_next_state(UpperCAmelCase_ ,		string[i]				) is None
                    and current_state != 0
                ):
                       lowercase_	    =			self.adlist[current_state]["""fail_state"""]
                lowercase_	    =			self.find_next_state(UpperCAmelCase_ ,		string[i]				)
                if next_state is None:
                       lowercase_	    =			0
                else:
                       lowercase_	    =			next_state
                       for key in self.adlist[current_state]["output"]:
                              if key not in result:
                                     lowercase_	    =			[]
                              result[key].append(i - len(UpperCAmelCase_				) + 1				)
         return result


if __name__ == "__main__":
 import doctest

 doctest.testmod()








"""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)
