


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


SCREAMING_SNAKE_CASE						:Any			=  logging.get_logger(__name__)


class      __lowerCAmelCase  (			_a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'AutoTokenizer'
  _SCREAMING_SNAKE_CASE		     =							['tokenizer']

  _SCREAMING_SNAKE_CASE		     =							{
      'semantic_prompt': 1,
      'coarse_prompt': 2,
      'fine_prompt': 2,
  }



  def __init__(  self							:							Tuple     ,					_lowerCAmelCase							:							str     ,					_lowerCAmelCase							:							List[Any]=None	) ->			Dict:




     """simple docstring"""
     super().__init__(lowerCAmelCase_	)

     snake_case_            =       speaker_embeddings



  @classmethod
  def        lowerCAmelCase__	(  cls							:							Any     ,					_lowerCAmelCase							:							int     ,					_lowerCAmelCase							:							List[Any]="speaker_embeddings_path.json"     ,					**_lowerCAmelCase							:							Any	) ->			str:




     """simple docstring"""


     if speaker_embeddings_dict_path is not None:
        snake_case_            =       get_file_from_repo(
            lowerCAmelCase_     ,					lowerCAmelCase_     ,					subfolder=kwargs.pop("subfolder"     ,					lowerCAmelCase_	)     ,					cache_dir=kwargs.pop("cache_dir"     ,					lowerCAmelCase_	)     ,					force_download=kwargs.pop("force_download"     ,					lowerCAmelCase_	)     ,					proxies=kwargs.pop("proxies"     ,					lowerCAmelCase_	)     ,					resume_download=kwargs.pop("resume_download"     ,					lowerCAmelCase_	)     ,					local_files_only=kwargs.pop("local_files_only"     ,					lowerCAmelCase_	)     ,					use_auth_token=kwargs.pop("use_auth_token"     ,					lowerCAmelCase_	)     ,					revision=kwargs.pop("revision"     ,					lowerCAmelCase_	)     ,					)
        if speaker_embeddings_path is None:
           logger.warning(
               F'''`{os.path.join(lowerCAmelCase_     ,					lowerCAmelCase_	)}` 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`.'''	)
           snake_case_            =       None
        else:
           with open(lowerCAmelCase_	) as speaker_embeddings_json:
              snake_case_            =       json.load(lowerCAmelCase_	)
     else:
        snake_case_            =       None

     snake_case_            =       AutoTokenizer.from_pretrained(lowerCAmelCase_     ,					**lowerCAmelCase_	)

     return cls(tokenizer=lowerCAmelCase_     ,					speaker_embeddings=lowerCAmelCase_	)



  def        lowerCAmelCase__	(  self							:							Optional[int]     ,					_lowerCAmelCase							:							Any     ,					_lowerCAmelCase							:							Optional[Any]="speaker_embeddings_path.json"     ,					_lowerCAmelCase							:							Dict="speaker_embeddings"     ,					_lowerCAmelCase							:							bool = False     ,					**_lowerCAmelCase							:							List[str]     ,					) ->			str:




     """simple docstring"""


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

        snake_case_            =       {}

        snake_case_            =       save_directory

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

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

              snake_case_            =       tmp_dict

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

     super().save_pretrained(lowerCAmelCase_     ,					lowerCAmelCase_     ,					**lowerCAmelCase_	)



  def        lowerCAmelCase__	(  self							:							List[str]     ,					_lowerCAmelCase							:							str = None     ,					**_lowerCAmelCase							:							Union[str, Any]	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.speaker_embeddings[voice_preset]

     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}].'''	)

        snake_case_            =       get_file_from_repo(
            self.speaker_embeddings.get("repo_or_path"     ,					"/"	)     ,					voice_preset_paths[key]     ,					subfolder=kwargs.pop("subfolder"     ,					lowerCAmelCase_	)     ,					cache_dir=kwargs.pop("cache_dir"     ,					lowerCAmelCase_	)     ,					force_download=kwargs.pop("force_download"     ,					lowerCAmelCase_	)     ,					proxies=kwargs.pop("proxies"     ,					lowerCAmelCase_	)     ,					resume_download=kwargs.pop("resume_download"     ,					lowerCAmelCase_	)     ,					local_files_only=kwargs.pop("local_files_only"     ,					lowerCAmelCase_	)     ,					use_auth_token=kwargs.pop("use_auth_token"     ,					lowerCAmelCase_	)     ,					revision=kwargs.pop("revision"     ,					lowerCAmelCase_	)     ,					)
        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.'''	)

        snake_case_            =       np.load(lowerCAmelCase_	)

     return voice_preset_dict



  def        lowerCAmelCase__	(  self							:							int     ,					_lowerCAmelCase							:							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							:							Union[str, Any]     ,					_lowerCAmelCase							:							int=None     ,					_lowerCAmelCase							:							Union[str, Any]=None     ,					_lowerCAmelCase							:							Dict="pt"     ,					_lowerCAmelCase							:							Dict=2_5_6     ,					_lowerCAmelCase							:							Tuple=False     ,					_lowerCAmelCase							:							Union[str, Any]=True     ,					_lowerCAmelCase							:							Union[str, Any]=False     ,					**_lowerCAmelCase							:							int     ,					) ->			str:




     """simple docstring"""


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

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

           snake_case_            =       np.load(lowerCAmelCase_	)

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

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

     if voice_preset is not None:
        snake_case_            =       voice_preset

     return encoded_text










'''simple docstring'''


from collections import OrderedDict
from typing import Mapping

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


_snake_case : Optional[int]  				=						logging.get_logger(__name__)

_snake_case : Any  				=						{
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}

class      A       (						_a       ):
							lowercase_							     =							'roformer'







							def __init__(    self			:    str				,     lowerCAmelCase_			:    int=5_00_00				,     lowerCAmelCase_			:    Any=None				,     lowerCAmelCase_			:    int=7_68				,     lowerCAmelCase_			:    Tuple=12				,     lowerCAmelCase_			:    Any=12				,     lowerCAmelCase_			:    List[str]=30_72				,     lowerCAmelCase_			:    Dict="gelu"				,     lowerCAmelCase_			:    Optional[int]=0.1				,     lowerCAmelCase_			:    List[Any]=0.1				,     lowerCAmelCase_			:    int=15_36				,     lowerCAmelCase_			:    Optional[Any]=2				,     lowerCAmelCase_			:    int=0.0_2				,     lowerCAmelCase_			:    Dict=1e-12				,     lowerCAmelCase_			:    Any=0				,     lowerCAmelCase_			:    Optional[Any]=False				,     lowerCAmelCase_			:    Tuple=True				,     **lowerCAmelCase_			:    Optional[int]				,     )     ->				str:


														"""simple docstring"""


														super().__init__(pad_token_id=lowerCAmelCase_				,     **lowerCAmelCase_   )

														_a      	=							vocab_size
														_a      	=							hidden_size if embedding_size is None else embedding_size
														_a      	=							hidden_size
														_a      	=							num_hidden_layers
														_a      	=							num_attention_heads
														_a      	=							hidden_act
														_a      	=							intermediate_size
														_a      	=							hidden_dropout_prob
														_a      	=							attention_probs_dropout_prob
														_a      	=							max_position_embeddings
														_a      	=							type_vocab_size
														_a      	=							initializer_range
														_a      	=							layer_norm_eps
														_a      	=							rotary_value
														_a      	=							use_cache



class      A       (						_a       ):







							@property
							def   __lowerCAmelCase     (    self			:    Any   )     ->				Mapping[str, Mapping[int, str]]:


														"""simple docstring"""


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

