
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__		:     Any     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Tuple 				=    '''AutoTokenizer'''
   __UpperCamelCase		:    str 				=    ['''tokenizer''']

   __UpperCamelCase		:    List[Any] 				=    {
       '''semantic_prompt''': 1,
       '''coarse_prompt''': 2,
       '''fine_prompt''': 2,
   }


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							List[str]			,			lowerCAmelCase_     :							Optional[int]=None     ):

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

      _A:  List[str]					=       speaker_embeddings


   @classmethod
   def  __magic_name__				(					cls     :							List[Any]			,			lowerCAmelCase_     :							Dict			,			lowerCAmelCase_     :							List[str]="speaker_embeddings_path.json"			,			**lowerCAmelCase_     :							Tuple     ):

      """simple docstring"""

      if speaker_embeddings_dict_path is not None:
         _A:  Union[str, Any]					=       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
                    , 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`."""     )
            _A:  Any					=       None
         else:
            with open(lowerCAmelCase_     ) as speaker_embeddings_json:
               _A:  Optional[int]					=       json.load(lowerCAmelCase_     )
      else:
         _A:  str					=       None

      _A:  List[str]					=       AutoTokenizer.from_pretrained(lowerCAmelCase_			,			**lowerCAmelCase_     )

      return cls(tokenizer=lowerCAmelCase_			,			speaker_embeddings=lowerCAmelCase_     )


   def  __magic_name__				(					self     :							int			,			lowerCAmelCase_     :							int			,			lowerCAmelCase_     :							Union[str, Any]="speaker_embeddings_path.json"			,			lowerCAmelCase_     :							str="speaker_embeddings"			,			lowerCAmelCase_     :							bool = False			,			**lowerCAmelCase_     :							int			,			):

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

         _A:  Dict					=       {}

         _A:  Tuple					=       save_directory

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

               _A:  str					=       {}
               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_			,			)
                  _A:  Optional[Any]					=       os.path.join(lowerCAmelCase_			,			F"""{prompt_key}_{key}.npy"""     )

               _A:  Any					=       tmp_dict

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

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


   def  __magic_name__				(					self     :							Dict			,			lowerCAmelCase_     :							str = None			,			**lowerCAmelCase_     :							List[str]     ):

      """simple docstring"""
      _A:  Union[str, Any]					=       self.speaker_embeddings[voice_preset]

      _A:  Optional[Any]					=       {}
      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}]."""     )

         _A:  str					=       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
                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
                    embeddings."""     )

         _A:  Dict					=       np.load(lowerCAmelCase_     )

      return voice_preset_dict


   def  __magic_name__				(					self     :							Tuple			,			lowerCAmelCase_     :							Optional[dict] = None     ):

      """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     :							Any			,			lowerCAmelCase_     :							int=None			,			lowerCAmelCase_     :							Any=None			,			lowerCAmelCase_     :							Tuple="pt"			,			lowerCAmelCase_     :							Optional[int]=2_5_6			,			lowerCAmelCase_     :							List[Any]=False			,			lowerCAmelCase_     :							str=True			,			lowerCAmelCase_     :							int=False			,			**lowerCAmelCase_     :							Optional[Any]			,			):

      """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
         ):
            _A:  Optional[int]					=       self._load_voice_preset(lowerCAmelCase_     )

         else:
            if isinstance(lowerCAmelCase_			,			lowerCAmelCase_     ) and not voice_preset.endswith('''.npz'''     ):
               _A:  List[str]					=       voice_preset + '''.npz'''

            _A:  List[str]					=       np.load(lowerCAmelCase_     )

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

      _A:  Optional[int]					=       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:
         _A:  Optional[Any]					=       voice_preset

      return encoded_text





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__		:     Any     			=      {'configuration_swin': ['SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SwinConfig', 'SwinOnnxConfig']}


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     Union[str, Any]     			=      [
       'SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SwinForImageClassification',
       'SwinForMaskedImageModeling',
       'SwinModel',
       'SwinPreTrainedModel',
       'SwinBackbone',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   UpperCAmelCase__		:     str     			=      [
       'TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFSwinForImageClassification',
       'TFSwinForMaskedImageModeling',
       'TFSwinModel',
       'TFSwinPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_swin import SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, SwinConfig, SwinOnnxConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_swin import (
          SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          SwinBackbone,
          SwinForImageClassification,
          SwinForMaskedImageModeling,
          SwinModel,
          SwinPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_swin import (
          TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFSwinForImageClassification,
          TFSwinForMaskedImageModeling,
          TFSwinModel,
          TFSwinPreTrainedModel,
      )

else:
   import sys

   UpperCAmelCase__		:     Tuple     			=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



