
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


_A						      =				logging.get_logger(__name__)







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Any     =	"AutoTokenizer"
     UpperCAmelCase__       :							str     =	["tokenizer"]

     UpperCAmelCase__       :							List[Any]     =	{
         "semantic_prompt": 1,
         "coarse_prompt": 2,
         "fine_prompt": 2,
     }





     def __init__(self   :					str       ,					_A   :					int       ,					_A   :					Any=None     )		->		Tuple:
         super().__init__(_A     )

         snake_case				  =      speaker_embeddings





     @classmethod
     def 	UpperCAmelCase(cls   :					List[Any]       ,					_A   :					Any       ,					_A   :					Tuple="speaker_embeddings_path.json"       ,					**_A   :					Any     )		->		Dict:

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

         snake_case				  =      AutoTokenizer.from_pretrained(_A       ,					**_A     )

         return cls(tokenizer=_A       ,					speaker_embeddings=_A     )





     def 	UpperCAmelCase(self   :					Union[str, Any]       ,					_A   :					int       ,					_A   :					Union[str, Any]="speaker_embeddings_path.json"       ,					_A   :					Optional[Any]="speaker_embeddings"       ,					_A   :					bool = False       ,					**_A   :					Union[str, Any]       ,					)		->		Tuple:
         if self.speaker_embeddings is not None:
             os.makedirs(os.path.join(_A       ,					_A       ,					"v2"     )       ,					exist_ok=_A     )

             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(_A     )

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

                     snake_case				  =      tmp_dict

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

         super().save_pretrained(_A       ,					_A       ,					**_A     )





     def 	UpperCAmelCase(self   :					List[Any]       ,					_A   :					str = None       ,					**_A   :					Dict     )		->		Tuple:
         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"       ,					_A     )       ,					cache_dir=kwargs.pop("cache_dir"       ,					_A     )       ,					force_download=kwargs.pop("force_download"       ,					_A     )       ,					proxies=kwargs.pop("proxies"       ,					_A     )       ,					resume_download=kwargs.pop("resume_download"       ,					_A     )       ,					local_files_only=kwargs.pop("local_files_only"       ,					_A     )       ,					use_auth_token=kwargs.pop("use_auth_token"       ,					_A     )       ,					revision=kwargs.pop("revision"       ,					_A     )       ,					)
             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(_A     )

         return voice_preset_dict





     def 	UpperCAmelCase(self   :					Dict       ,					_A   :					Optional[dict] = None     )		->		str:
         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       ,					_A   :					Optional[int]=None       ,					_A   :					Dict=None       ,					_A   :					Optional[int]="pt"       ,					_A   :					List[Any]=2_5_6       ,					_A   :					Union[str, Any]=False       ,					_A   :					List[Any]=True       ,					_A   :					int=False       ,					**_A   :					Tuple       ,					)		->		Union[str, Any]:
         if voice_preset is not None and not isinstance(_A       ,					_A     ):
             if (
                 isinstance(_A       ,					_A     )
                 and self.speaker_embeddings is not None
                 and voice_preset in self.speaker_embeddings
             ):
                 snake_case				  =      self._load_voice_preset(_A     )

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

                 snake_case				  =      np.load(_A     )

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

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

         if voice_preset is not None:
             snake_case				  =      voice_preset

         return encoded_text



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						      =				logging.get_logger(__name__)

_A						      =				{
    "google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}







class   lowerCamelCase	(						A_						):
     UpperCAmelCase__       :							Dict     =	"canine"





     def __init__(self   :					List[Any]       ,					_A   :					Union[str, Any]=7_6_8       ,					_A   :					Any=1_2       ,					_A   :					List[Any]=1_2       ,					_A   :					List[Any]=3_0_7_2       ,					_A   :					Dict="gelu"       ,					_A   :					Optional[Any]=0.1       ,					_A   :					Tuple=0.1       ,					_A   :					str=1_6_3_8_4       ,					_A   :					Union[str, Any]=1_6       ,					_A   :					Any=0.02       ,					_A   :					List[str]=1E-12       ,					_A   :					Union[str, Any]=0       ,					_A   :					Dict=0Xe0_00       ,					_A   :					List[Any]=0Xe0_01       ,					_A   :					int=4       ,					_A   :					str=4       ,					_A   :					Optional[int]=8       ,					_A   :					Optional[Any]=1_6_3_8_4       ,					_A   :					Optional[Any]=1_2_8       ,					**_A   :					Union[str, Any]       ,					)		->		Union[str, Any]:
         super().__init__(pad_token_id=_A       ,					bos_token_id=_A       ,					eos_token_id=_A       ,					**_A     )

         snake_case				  =      max_position_embeddings
         snake_case				  =      hidden_size
         snake_case				  =      num_hidden_layers
         snake_case				  =      num_attention_heads
         snake_case				  =      intermediate_size
         snake_case				  =      hidden_act
         snake_case				  =      hidden_dropout_prob
         snake_case				  =      attention_probs_dropout_prob
         snake_case				  =      initializer_range
         snake_case				  =      type_vocab_size
         snake_case				  =      layer_norm_eps

         # Character config:
         snake_case				  =      downsampling_rate
         snake_case				  =      upsampling_kernel_size
         snake_case				  =      num_hash_functions
         snake_case				  =      num_hash_buckets
         snake_case				  =      local_transformer_stride

