






'''simple docstring'''
import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCamelCase__			=      'src/diffusers'

# Matches is_xxx_available()
UpperCamelCase__			=      re.compile(r'is\_([a-z_]*)_available\(\)')
# Matches from xxx import bla
UpperCamelCase__			=      re.compile(r'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')


UpperCamelCase__			=      '\n{0} = None\n'

UpperCamelCase__			=      '\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n\n    @classmethod\n    def from_config(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n\n    @classmethod\n    def from_pretrained(cls, *args, **kwargs):\n        requires_backends(cls, {1})\n'


UpperCamelCase__			=      '\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n'




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase				):


   """simple docstring"""




   lowercase_						:     Tuple							     =  _re_backend.findall(_UpperCamelCase				)
   if len(_UpperCamelCase				) == 0:
      return None

   return "_and_".join(_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	):


   """simple docstring"""




   with open(os.path.join(_UpperCamelCase    ,		"__init__.py"				)    ,		"r"    ,		encoding="utf-8"    ,		newline="\n"				) as f:
      lowercase_						:     Any							     =  f.readlines()

   # Get to the point we do the actual imports for type checking
   lowercase_						:     Optional[int]							     =  0
   lowercase_						:     Union[str, Any]							     =  {}
   # Go through the end of the file
   while line_index < len(_UpperCamelCase				):
      # If the line contains is_backend_available, we grab all objects associated with the `else` block
      lowercase_						:     str							     =  find_backend(lines[line_index]				)
      if backend is not None:
         while not lines[line_index].startswith("else:"				):
            line_index += 1
         line_index += 1
         lowercase_						:     int							     =  []
         # Until we unindent, add backend objects to the list
         while line_index < len(_UpperCamelCase				) and len(lines[line_index]				) > 1:
            lowercase_						:     List[str]							     =  lines[line_index]
            lowercase_						:     Tuple							     =  _re_single_line_import.search(_UpperCamelCase				)
            if single_line_import_search is not None:
               objects.extend(single_line_import_search.groups()[0].split(", "				)				)
            elif line.startswith(" " * 8				):
               objects.append(line[8:-2]				)
            line_index += 1

         if len(_UpperCamelCase				) > 0:
            lowercase_						:     List[Any]							     =  objects
      else:
         line_index += 1

   return backend_specific_objects




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   if name.isupper():
      return DUMMY_CONSTANT.format(_UpperCamelCase				)
   elif name.islower():
      return DUMMY_FUNCTION.format(_UpperCamelCase    ,		_UpperCamelCase				)
   else:
      return DUMMY_CLASS.format(_UpperCamelCase    ,		_UpperCamelCase				)




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase=None				):


   """simple docstring"""




   if backend_specific_objects is None:
      lowercase_						:     Optional[int]							     =  read_init()
   # For special correspondence backend to module name as used in the function requires_modulename
   lowercase_						:     List[Any]							     =  {}

   for backend, objects in backend_specific_objects.items():
      lowercase_						:     List[str]							     =  "[" + ", ".join(F"""\"{b}\"""" for b in backend.split("_and_"				)				) + "]"
      lowercase_						:     Union[str, Any]							     =  "# This file is autogenerated by the command `make fix-copies`, do not edit.\n"
      dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
      dummy_file += "\n".join([create_dummy_object(_UpperCamelCase    ,		_UpperCamelCase				) for o in objects]				)
      lowercase_						:     Dict							     =  dummy_file

   return dummy_files




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase=False				):


   """simple docstring"""




   lowercase_						:     int							     =  create_dummy_files()
   # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
   lowercase_						:     List[str]							     =  {"torch": "pt"}

   # Locate actual dummy modules and read their content.
   lowercase_						:     str							     =  os.path.join(_UpperCamelCase    ,		"utils"				)
   lowercase_						:     Optional[int]							     =  {
       backend: os.path.join(_UpperCamelCase    ,		F"""dummy_{short_names.get(_UpperCamelCase    ,		_UpperCamelCase				)}_objects.py"""				)
       for backend in dummy_files.keys()
   }

   lowercase_						:     List[Any]							     =  {}
   for backend, file_path in dummy_file_paths.items():
      if os.path.isfile(_UpperCamelCase				):
         with open(_UpperCamelCase    ,		"r"    ,		encoding="utf-8"    ,		newline="\n"				) as f:
            lowercase_						:     str							     =  f.read()
      else:
         lowercase_						:     Union[str, Any]							     =  ""

   for backend in dummy_files.keys():
      if dummy_files[backend] != actual_dummies[backend]:
         if overwrite:
            print(
                F"""Updating diffusers.utils.dummy_{short_names.get(_UpperCamelCase    ,		_UpperCamelCase				)}_objects.py as the main """
                "__init__ has new objects."				)
            with open(dummy_file_paths[backend]    ,		"w"    ,		encoding="utf-8"    ,		newline="\n"				) as f:
               f.write(dummy_files[backend]				)
         else:
            raise ValueError(
                "The main __init__ has objects that are not present in "
                F"""diffusers.utils.dummy_{short_names.get(_UpperCamelCase    ,		_UpperCamelCase				)}_objects.py. Run `make fix-copies` """
                "to fix this."				)


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

 check_dummies(args.fix_and_overwrite)












'''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)




