


"""simple docstring"""






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     __lowerCamelCase  (					a__				):

     '''simple docstring'''
     A_       :	Union[str, Any]    =		'AutoTokenizer'
     A_       :	Tuple    =		['tokenizer']

     A_       :	Any    =		{
         'semantic_prompt': 1,
         'coarse_prompt': 2,
         'fine_prompt': 2,
     }


     def __init__(      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase=None							)       ->							Optional[int]:
          super().__init__(__UpperCAmelCase							)

          _a    	=				speaker_embeddings


     @classmethod
     def 							_UpperCAmelCase      (      cls    ,				__UpperCAmelCase    ,				__UpperCAmelCase="speaker_embeddings_path.json"    ,				**__UpperCAmelCase							)       ->							str:

          if speaker_embeddings_dict_path is not None:
               _a    	=				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\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`.'							)
                    _a    	=				None
               else:
                    with open(__UpperCAmelCase							) as speaker_embeddings_json:
                         _a    	=				json.load(__UpperCAmelCase							)
          else:
               _a    	=				None

          _a    	=				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    ,				)       ->							Dict:
          if self.speaker_embeddings is not None:
               os.makedirs(os.path.join(__UpperCAmelCase    ,				__UpperCAmelCase    ,				'''v2'''							)    ,				exist_ok=__UpperCAmelCase							)

               _a    	=				{}

               _a    	=				save_directory

               for prompt_key in self.speaker_embeddings:
                    if prompt_key != "repo_or_path":
                         _a    	=				self._load_voice_preset(__UpperCAmelCase							)

                         _a    	=				{}
                         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    ,				)
                              _a    	=				os.path.join(__UpperCAmelCase    ,				F'{prompt_key}_{key}.npy'							)

                         _a    	=				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							)       ->							Optional[int]:
          _a    	=				self.speaker_embeddings[voice_preset]

          _a    	=				{}
          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    	=				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\n                    , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n                    embeddings.'							)

               _a    	=				np.load(__UpperCAmelCase							)

          return voice_preset_dict


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase = None							)       ->							int:
          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    ,				)       ->							str:
          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
               ):
                    _a    	=				self._load_voice_preset(__UpperCAmelCase							)

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

                    _a    	=				np.load(__UpperCAmelCase							)

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

          _a    	=				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:
               _a    	=				voice_preset

          return encoded_text



"""simple docstring"""






from typing import TYPE_CHECKING

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


__snake_case		=       {
    '''configuration_pix2struct''': [
        '''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''Pix2StructConfig''',
        '''Pix2StructTextConfig''',
        '''Pix2StructVisionConfig''',
    ],
    '''processing_pix2struct''': ['''Pix2StructProcessor'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       ['''Pix2StructImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   __snake_case		=       [
       '''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''Pix2StructPreTrainedModel''',
       '''Pix2StructForConditionalGeneration''',
       '''Pix2StructVisionModel''',
       '''Pix2StructTextModel''',
   ]

if TYPE_CHECKING:
   from .configuration_pixastruct import (
       PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
       PixaStructConfig,
       PixaStructTextConfig,
       PixaStructVisionConfig,
   )
   from .processing_pixastruct import PixaStructProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_pixastruct import PixaStructImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_pixastruct import (
          PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
          PixaStructForConditionalGeneration,
          PixaStructPreTrainedModel,
          PixaStructTextModel,
          PixaStructVisionModel,
      )

else:
   import sys

   __snake_case		=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)