

'''simple docstring'''



import json
import os
from typing import Optional

import numpy as np

from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer


UpperCAmelCase					:			List[str]       =	logging.get_logger(__name__)







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = "AutoTokenizer"
 lowerCAmelCase__       = ["tokenizer"]

 lowerCAmelCase__       = {
     "semantic_prompt": 1,
     "coarse_prompt": 2,
     "fine_prompt": 2,
 }
 def __init__(						self   :			str   ,				__SCREAMING_SNAKE_CASE   :			str   ,				__SCREAMING_SNAKE_CASE   :			str=None     )    ->					Optional[Any]:


      """simple docstring"""




      super().__init__(__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = speaker_embeddings
 @classmethod
 def  UpperCAmelCase__						(						cls   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			List[str]="speaker_embeddings_path.json"   ,				**__SCREAMING_SNAKE_CASE   :			Optional[Any]     )    ->					Optional[Any]:


      """simple docstring"""




      if speaker_embeddings_dict_path is not None:
           __SCREAMING_SNAKE_CASE      = get_file_from_repo(
               __SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				subfolder=kwargs.pop("""subfolder"""   ,				__SCREAMING_SNAKE_CASE     )   ,				cache_dir=kwargs.pop("""cache_dir"""   ,				__SCREAMING_SNAKE_CASE     )   ,				force_download=kwargs.pop("""force_download"""   ,				__SCREAMING_SNAKE_CASE     )   ,				proxies=kwargs.pop("""proxies"""   ,				__SCREAMING_SNAKE_CASE     )   ,				resume_download=kwargs.pop("""resume_download"""   ,				__SCREAMING_SNAKE_CASE     )   ,				local_files_only=kwargs.pop("""local_files_only"""   ,				__SCREAMING_SNAKE_CASE     )   ,				use_auth_token=kwargs.pop("""use_auth_token"""   ,				__SCREAMING_SNAKE_CASE     )   ,				revision=kwargs.pop("""revision"""   ,				__SCREAMING_SNAKE_CASE     )   ,				)
           if speaker_embeddings_path is None:
                logger.warning(
                    f'`{os.path.join(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )}` does not exists\n                    , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n                    dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.'     )
                __SCREAMING_SNAKE_CASE      = None
           else:
                with open(__SCREAMING_SNAKE_CASE     ) as speaker_embeddings_json:
                     __SCREAMING_SNAKE_CASE      = json.load(__SCREAMING_SNAKE_CASE     )
      else:
           __SCREAMING_SNAKE_CASE      = None

      __SCREAMING_SNAKE_CASE      = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

      return cls(tokenizer=__SCREAMING_SNAKE_CASE   ,				speaker_embeddings=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Dict   ,				__SCREAMING_SNAKE_CASE   :			str="speaker_embeddings_path.json"   ,				__SCREAMING_SNAKE_CASE   :			Any="speaker_embeddings"   ,				__SCREAMING_SNAKE_CASE   :			bool = False   ,				**__SCREAMING_SNAKE_CASE   :			Optional[Any]   ,				)    ->					Optional[int]:


      """simple docstring"""




      if self.speaker_embeddings is not None:
           os.makedirs(os.path.join(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				"""v2"""     )   ,				exist_ok=__SCREAMING_SNAKE_CASE     )

           __SCREAMING_SNAKE_CASE      = {}

           __SCREAMING_SNAKE_CASE      = save_directory

           for prompt_key in self.speaker_embeddings:
                if prompt_key != "repo_or_path":
                     __SCREAMING_SNAKE_CASE      = self._load_voice_preset(__SCREAMING_SNAKE_CASE     )

                     __SCREAMING_SNAKE_CASE      = {}
                     for key in self.speaker_embeddings[prompt_key]:
                          np.save(
                              os.path.join(
                                  embeddings_dict["""repo_or_path"""]   ,				__SCREAMING_SNAKE_CASE   ,				f'{prompt_key}_{key}'     )   ,				voice_preset[key]   ,				allow_pickle=__SCREAMING_SNAKE_CASE   ,				)
                          __SCREAMING_SNAKE_CASE      = os.path.join(__SCREAMING_SNAKE_CASE   ,				f'{prompt_key}_{key}.npy'     )

                     __SCREAMING_SNAKE_CASE      = tmp_dict

           with open(os.path.join(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )   ,				"""w"""     ) as fp:
                json.dump(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )

      super().save_pretrained(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			Optional[int]   ,				__SCREAMING_SNAKE_CASE   :			str = None   ,				**__SCREAMING_SNAKE_CASE   :			Union[str, Any]     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = self.speaker_embeddings[voice_preset]

      __SCREAMING_SNAKE_CASE      = {}
      for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
           if key not in voice_preset_paths:
                raise ValueError(
                    f'Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].'     )

           __SCREAMING_SNAKE_CASE      = get_file_from_repo(
               self.speaker_embeddings.get("""repo_or_path"""   ,				"""/"""     )   ,				voice_preset_paths[key]   ,				subfolder=kwargs.pop("""subfolder"""   ,				__SCREAMING_SNAKE_CASE     )   ,				cache_dir=kwargs.pop("""cache_dir"""   ,				__SCREAMING_SNAKE_CASE     )   ,				force_download=kwargs.pop("""force_download"""   ,				__SCREAMING_SNAKE_CASE     )   ,				proxies=kwargs.pop("""proxies"""   ,				__SCREAMING_SNAKE_CASE     )   ,				resume_download=kwargs.pop("""resume_download"""   ,				__SCREAMING_SNAKE_CASE     )   ,				local_files_only=kwargs.pop("""local_files_only"""   ,				__SCREAMING_SNAKE_CASE     )   ,				use_auth_token=kwargs.pop("""use_auth_token"""   ,				__SCREAMING_SNAKE_CASE     )   ,				revision=kwargs.pop("""revision"""   ,				__SCREAMING_SNAKE_CASE     )   ,				)
           if path is None:
                raise ValueError(
                    f'`{os.path.join(self.speaker_embeddings.get("repo_or_path"   ,				"/"     )   ,				voice_preset_paths[key]     )}` does not exists\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'     )

           __SCREAMING_SNAKE_CASE      = np.load(__SCREAMING_SNAKE_CASE     )

      return voice_preset_dict
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[dict] = None     )    ->					Any:


      """simple docstring"""




      for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
           if key not in voice_preset:
                raise ValueError(f'Voice preset unrecognized, missing {key} as a key.'     )

           if not isinstance(voice_preset[key]   ,				np.ndarray     ):
                raise ValueError(f'{key} voice preset must be a {str(self.preset_shape[key]     )}D ndarray.'     )

           if len(voice_preset[key].shape     ) != self.preset_shape[key]:
                raise ValueError(f'{key} voice preset must be a {str(self.preset_shape[key]     )}D ndarray.'     )


 def __call__(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Tuple="pt"   ,				__SCREAMING_SNAKE_CASE   :			str=256   ,				__SCREAMING_SNAKE_CASE   :			List[Any]=False   ,				__SCREAMING_SNAKE_CASE   :			Tuple=True   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=False   ,				**__SCREAMING_SNAKE_CASE   :			Optional[int]   ,				)    ->					List[str]:


      """simple docstring"""




      if voice_preset is not None and not isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ):
           if (
               isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
               and self.speaker_embeddings is not None
               and voice_preset in self.speaker_embeddings
           ):
                __SCREAMING_SNAKE_CASE      = self._load_voice_preset(__SCREAMING_SNAKE_CASE     )

           else:
                if isinstance(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     ) and not voice_preset.endswith(""".npz"""     ):
                     __SCREAMING_SNAKE_CASE      = voice_preset + """.npz"""

                __SCREAMING_SNAKE_CASE      = np.load(__SCREAMING_SNAKE_CASE     )

      if voice_preset is not None:
           self._validate_voice_preset_dict(__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
           __SCREAMING_SNAKE_CASE      = BatchFeature(data=__SCREAMING_SNAKE_CASE   ,				tensor_type=__SCREAMING_SNAKE_CASE     )

      __SCREAMING_SNAKE_CASE      = self.tokenizer(
          __SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				padding="""max_length"""   ,				max_length=__SCREAMING_SNAKE_CASE   ,				return_attention_mask=__SCREAMING_SNAKE_CASE   ,				return_token_type_ids=__SCREAMING_SNAKE_CASE   ,				add_special_tokens=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE   ,				)

      if voice_preset is not None:
           __SCREAMING_SNAKE_CASE      = voice_preset

      return encoded_text









'''simple docstring'''



import importlib
import inspect
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_config_docstrings.py
UpperCAmelCase					:			Any       =	'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase					:			Optional[int]       =	importlib.util.spec_from_file_location(
    'transformers',
    os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
UpperCAmelCase					:			Dict       =	spec.loader.load_module()

UpperCAmelCase					:			Dict       =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCAmelCase					:			Dict       =	re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')


UpperCAmelCase					:			Any       =	{
    'CLIPConfigMixin',
    'DecisionTransformerConfigMixin',
    'EncoderDecoderConfigMixin',
    'RagConfigMixin',
    'SpeechEncoderDecoderConfigMixin',
    'VisionEncoderDecoderConfigMixin',
    'VisionTextDualEncoderConfigMixin',
}







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []

     for config_class in list(CONFIG_MAPPING.values()		):
          __SCREAMING_SNAKE_CASE      = False

          # source code of `config_class`
          __SCREAMING_SNAKE_CASE      = inspect.getsource(a__		)
          __SCREAMING_SNAKE_CASE      = _re_checkpoint.findall(a__		)

          for checkpoint in checkpoints:
               # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
               # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = checkpoint

               # verify the checkpoint name corresponds to the checkpoint link
               __SCREAMING_SNAKE_CASE      = F'https://huggingface.co/{ckpt_name}'
               if ckpt_link == ckpt_link_from_name:
                    __SCREAMING_SNAKE_CASE      = True
                    break

          __SCREAMING_SNAKE_CASE      = config_class.__name__
          if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
               configs_without_checkpoint.append(a__		)

     if len(a__		) > 0:
          __SCREAMING_SNAKE_CASE      = """\n""".join(sorted(a__		)		)
          raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'		)


if __name__ == "__main__":
  check_config_docstrings_have_checkpoints()






