






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


snake_case    				=		logging.get_logger(__name__)


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Optional[Any]			=	'''AutoTokenizer'''
     UpperCamelCase_							:		Optional[int]			=	['''tokenizer''']

     UpperCamelCase_							:		Optional[int]			=	{
         '''semantic_prompt''': 1,
         '''coarse_prompt''': 2,
         '''fine_prompt''': 2,
     }


     def __init__(				self       :	Tuple					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Tuple=None      ):
       super().__init__(UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    speaker_embeddings


     @classmethod
     def 				_A       (				cls       :	Dict					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Optional[Any]="speaker_embeddings_path.json"					, **UpperCAmelCase_       :	List[str]      ):

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

       SCREAMING_SNAKE_CASE       :      int							=    AutoTokenizer.from_pretrained(UpperCAmelCase_					, **UpperCAmelCase_      )

       return cls(tokenizer=UpperCAmelCase_					, speaker_embeddings=UpperCAmelCase_      )


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	Dict					, UpperCAmelCase_       :	Dict="speaker_embeddings_path.json"					, UpperCAmelCase_       :	str="speaker_embeddings"					, UpperCAmelCase_       :	bool = False					, **UpperCAmelCase_       :	List[Any]					, ):
       if self.speaker_embeddings is not None:
         os.makedirs(os.path.join(UpperCAmelCase_					, UpperCAmelCase_					, "v2"      )					, exist_ok=UpperCAmelCase_      )

         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {}

         SCREAMING_SNAKE_CASE       :      int							=    save_directory

         for prompt_key in self.speaker_embeddings:
           if prompt_key != "repo_or_path":
             SCREAMING_SNAKE_CASE       :      Any							=    self._load_voice_preset(UpperCAmelCase_      )

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

             SCREAMING_SNAKE_CASE       :      Optional[Any]							=    tmp_dict

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

       super().save_pretrained(UpperCAmelCase_					, UpperCAmelCase_					, **UpperCAmelCase_      )


     def 				_A       (				self       :	int					, UpperCAmelCase_       :	str = None					, **UpperCAmelCase_       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.speaker_embeddings[voice_preset]

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

         SCREAMING_SNAKE_CASE       :      Optional[int]							=    np.load(UpperCAmelCase_      )

       return voice_preset_dict


     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	Optional[dict] = None      ):
       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       :	int					, UpperCAmelCase_       :	Union[str, Any]=None					, UpperCAmelCase_       :	str=None					, UpperCAmelCase_       :	List[str]="pt"					, UpperCAmelCase_       :	List[str]=256					, UpperCAmelCase_       :	Optional[int]=False					, UpperCAmelCase_       :	Optional[int]=True					, UpperCAmelCase_       :	Dict=False					, **UpperCAmelCase_       :	List[Any]					, ):
       if voice_preset is not None and not isinstance(UpperCAmelCase_					, UpperCAmelCase_      ):
         if (
             isinstance(UpperCAmelCase_					, UpperCAmelCase_      )
             and self.speaker_embeddings is not None
             and voice_preset in self.speaker_embeddings
         ):
           SCREAMING_SNAKE_CASE       :      int							=    self._load_voice_preset(UpperCAmelCase_      )

         else:
           if isinstance(UpperCAmelCase_					, UpperCAmelCase_      ) and not voice_preset.endswith(".npz"      ):
             SCREAMING_SNAKE_CASE       :      List[Any]							=    voice_preset + ".npz"

           SCREAMING_SNAKE_CASE       :      int							=    np.load(UpperCAmelCase_      )

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

       SCREAMING_SNAKE_CASE       :      Tuple							=    self.tokenizer(
           UpperCAmelCase_					, return_tensors=UpperCAmelCase_					, padding="max_length"					, max_length=UpperCAmelCase_					, return_attention_mask=UpperCAmelCase_					, return_token_type_ids=UpperCAmelCase_					, add_special_tokens=UpperCAmelCase_					, **UpperCAmelCase_					, )

       if voice_preset is not None:
         SCREAMING_SNAKE_CASE       :      Tuple							=    voice_preset

       return encoded_text














from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case    				=		logging.get_logger(__name__)

snake_case    				=		{
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Dict			=	'''camembert'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	Optional[Any]=3_0522					, UpperCAmelCase_       :	Optional[int]=768					, UpperCAmelCase_       :	List[Any]=12					, UpperCAmelCase_       :	Union[str, Any]=12					, UpperCAmelCase_       :	Optional[Any]=3072					, UpperCAmelCase_       :	Optional[int]="gelu"					, UpperCAmelCase_       :	List[Any]=0.1					, UpperCAmelCase_       :	Dict=0.1					, UpperCAmelCase_       :	List[str]=512					, UpperCAmelCase_       :	List[Any]=2					, UpperCAmelCase_       :	Optional[int]=0.02					, UpperCAmelCase_       :	List[str]=1E-12					, UpperCAmelCase_       :	int=1					, UpperCAmelCase_       :	Union[str, Any]=0					, UpperCAmelCase_       :	List[str]=2					, UpperCAmelCase_       :	int="absolute"					, UpperCAmelCase_       :	Tuple=True					, UpperCAmelCase_       :	Optional[Any]=None					, **UpperCAmelCase_       :	Optional[int]					, ):
       super().__init__(pad_token_id=UpperCAmelCase_					, bos_token_id=UpperCAmelCase_					, eos_token_id=UpperCAmelCase_					, **UpperCAmelCase_      )

       SCREAMING_SNAKE_CASE       :      int							=    vocab_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    hidden_size
       SCREAMING_SNAKE_CASE       :      Any							=    num_hidden_layers
       SCREAMING_SNAKE_CASE       :      Dict							=    num_attention_heads
       SCREAMING_SNAKE_CASE       :      Dict							=    hidden_act
       SCREAMING_SNAKE_CASE       :      Tuple							=    intermediate_size
       SCREAMING_SNAKE_CASE       :      Any							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      int							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      Dict							=    max_position_embeddings
       SCREAMING_SNAKE_CASE       :      List[Any]							=    type_vocab_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      Dict							=    position_embedding_type
       SCREAMING_SNAKE_CASE       :      List[Any]							=    use_cache
       SCREAMING_SNAKE_CASE       :      str							=    classifier_dropout





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	Optional[int]      ):
       if self.task == "multiple-choice":
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    {0: "batch", 1: "choice", 2: "sequence"}
       else:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    {0: "batch", 1: "sequence"}
       return OrderedDict(
           [
               ("input_ids", dynamic_axis),
               ("attention_mask", dynamic_axis),
           ]      )






