





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_           =	logging.get_logger(__name__)




class 			__UpperCamelCase    (							A__    ):
     __A	:  List[str]							  =   """AutoTokenizer"""
     __A	:  str							  =   ["""tokenizer"""]

     __A	:  Union[str, Any]							  =   {
         """semantic_prompt""": 1,
         """coarse_prompt""": 2,
         """fine_prompt""": 2,
     }





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase=None					):
        super().__init__(_UpperCamelCase					)

        _UpperCAmelCase           =   speaker_embeddings





     @classmethod
     def 		UpperCamelCase(     cls				,							_UpperCamelCase				,							_UpperCamelCase="speaker_embeddings_path.json"				,							**_UpperCamelCase					):

        if speaker_embeddings_dict_path is not None:
           _UpperCAmelCase           =   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`.'''					)
              _UpperCAmelCase           =   None
           else:
              with open(_UpperCamelCase					) as speaker_embeddings_json:
                 _UpperCAmelCase           =   json.load(_UpperCamelCase					)
        else:
           _UpperCAmelCase           =   None

        _UpperCAmelCase           =   AutoTokenizer.from_pretrained(_UpperCamelCase				,							**_UpperCamelCase					)

        return cls(tokenizer=_UpperCamelCase				,							speaker_embeddings=_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase="speaker_embeddings_path.json"				,							_UpperCamelCase="speaker_embeddings"				,							_UpperCamelCase = False				,							**_UpperCamelCase				,							):
        if self.speaker_embeddings is not None:
           os.makedirs(os.path.join(_UpperCamelCase				,							_UpperCamelCase				,							'''v2'''					)				,							exist_ok=_UpperCamelCase					)

           _UpperCAmelCase           =   {}

           _UpperCAmelCase           =   save_directory

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

                 _UpperCAmelCase           =   {}
                 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				,							)
                    _UpperCAmelCase           =   os.path.join(_UpperCamelCase				,							f'''{prompt_key}_{key}.npy'''					)

                 _UpperCAmelCase           =   tmp_dict

           with open(os.path.join(_UpperCamelCase				,							_UpperCamelCase					)				,							'''w'''					) as fp:
              json.dump(_UpperCamelCase				,							_UpperCamelCase					)

        super().save_pretrained(_UpperCamelCase				,							_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase = None				,							**_UpperCamelCase					):
        _UpperCAmelCase           =   self.speaker_embeddings[voice_preset]

        _UpperCAmelCase           =   {}
        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}].'''					)

           _UpperCAmelCase           =   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.'''					)

           _UpperCAmelCase           =   np.load(_UpperCamelCase					)

        return voice_preset_dict





     def 		UpperCamelCase(     self				,							_UpperCamelCase = 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				,							_UpperCamelCase=None				,							_UpperCamelCase=None				,							_UpperCamelCase="pt"				,							_UpperCamelCase=256				,							_UpperCamelCase=False				,							_UpperCamelCase=True				,							_UpperCamelCase=False				,							**_UpperCamelCase				,							):
        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
           ):
              _UpperCAmelCase           =   self._load_voice_preset(_UpperCamelCase					)

           else:
              if isinstance(_UpperCamelCase				,							_UpperCamelCase					) and not voice_preset.endswith('''.npz'''					):
                 _UpperCAmelCase           =   voice_preset + '''.npz'''

              _UpperCAmelCase           =   np.load(_UpperCamelCase					)

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

        _UpperCAmelCase           =   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:
           _UpperCAmelCase           =   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


a  :      Tuple            =    logging.get_logger(__name__)

a  :      List[str]            =    {
    """xlm-mlm-en-2048""": """https://huggingface.co/xlm-mlm-en-2048/resolve/main/config.json""",
    """xlm-mlm-ende-1024""": """https://huggingface.co/xlm-mlm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-enfr-1024""": """https://huggingface.co/xlm-mlm-enfr-1024/resolve/main/config.json""",
    """xlm-mlm-enro-1024""": """https://huggingface.co/xlm-mlm-enro-1024/resolve/main/config.json""",
    """xlm-mlm-tlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-tlm-xnli15-1024/resolve/main/config.json""",
    """xlm-mlm-xnli15-1024""": """https://huggingface.co/xlm-mlm-xnli15-1024/resolve/main/config.json""",
    """xlm-clm-enfr-1024""": """https://huggingface.co/xlm-clm-enfr-1024/resolve/main/config.json""",
    """xlm-clm-ende-1024""": """https://huggingface.co/xlm-clm-ende-1024/resolve/main/config.json""",
    """xlm-mlm-17-1280""": """https://huggingface.co/xlm-mlm-17-1280/resolve/main/config.json""",
    """xlm-mlm-100-1280""": """https://huggingface.co/xlm-mlm-100-1280/resolve/main/config.json""",
}

class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:				Any          =    'xlm'
    SCREAMING_SNAKE_CASE:				Optional[int]          =    {
        'hidden_size': 'emb_dim',
        'num_attention_heads': 'n_heads',
        'num_hidden_layers': 'n_layers',
        'n_words': 'vocab_size',  # For backward compatibility
    }




    def __init__(    self       ,						lowerCamelCase__=30_145       ,						lowerCamelCase__=2_048       ,						lowerCamelCase__=12       ,						lowerCamelCase__=16       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False       ,						lowerCamelCase__=1       ,						lowerCamelCase__=True       ,						lowerCamelCase__=512       ,						lowerCamelCase__=2_048**-0.5       ,						lowerCamelCase__=1E-12       ,						lowerCamelCase__=0.0_2       ,						lowerCamelCase__=0       ,						lowerCamelCase__=1       ,						lowerCamelCase__=2       ,						lowerCamelCase__=3       ,						lowerCamelCase__=5       ,						lowerCamelCase__=True       ,						lowerCamelCase__="first"       ,						lowerCamelCase__=True       ,						lowerCamelCase__=None       ,						lowerCamelCase__=True       ,						lowerCamelCase__=0.1       ,						lowerCamelCase__=5       ,						lowerCamelCase__=5       ,						lowerCamelCase__=0       ,						lowerCamelCase__=0       ,						lowerCamelCase__=2       ,						lowerCamelCase__=0       ,						**lowerCamelCase__       ,						):
       lowerCAmelCase_:							Any       =	vocab_size
       lowerCAmelCase_:							int       =	emb_dim
       lowerCAmelCase_:							int       =	n_layers
       lowerCAmelCase_:							Optional[int]       =	n_heads
       lowerCAmelCase_:							Tuple       =	dropout
       lowerCAmelCase_:							Tuple       =	attention_dropout
       lowerCAmelCase_:							Tuple       =	gelu_activation
       lowerCAmelCase_:							Dict       =	sinusoidal_embeddings
       lowerCAmelCase_:							str       =	causal
       lowerCAmelCase_:							Tuple       =	asm
       lowerCAmelCase_:							Any       =	n_langs
       lowerCAmelCase_:							int       =	use_lang_emb
       lowerCAmelCase_:							Dict       =	layer_norm_eps
       lowerCAmelCase_:							Dict       =	bos_index
       lowerCAmelCase_:							Union[str, Any]       =	eos_index
       lowerCAmelCase_:							Optional[Any]       =	pad_index
       lowerCAmelCase_:							List[Any]       =	unk_index
       lowerCAmelCase_:							Dict       =	mask_index
       lowerCAmelCase_:							Any       =	is_encoder
       lowerCAmelCase_:							Dict       =	max_position_embeddings
       lowerCAmelCase_:							int       =	embed_init_std
       lowerCAmelCase_:							Optional[int]       =	init_std
       lowerCAmelCase_:							str       =	summary_type
       lowerCAmelCase_:							Union[str, Any]       =	summary_use_proj
       lowerCAmelCase_:							Dict       =	summary_activation
       lowerCAmelCase_:							List[Any]       =	summary_proj_to_labels
       lowerCAmelCase_:							List[str]       =	summary_first_dropout
       lowerCAmelCase_:							List[str]       =	start_n_top
       lowerCAmelCase_:							List[Any]       =	end_n_top
       lowerCAmelCase_:							Any       =	mask_token_id
       lowerCAmelCase_:							List[Any]       =	lang_id

       if "n_words" in kwargs:
          lowerCAmelCase_:							Union[str, Any]       =	kwargs["n_words"]

       super().__init__(pad_token_id=lowerCamelCase__       ,						bos_token_id=lowerCamelCase__       ,						**lowerCamelCase__ )





class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




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