





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 typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class 			__UpperCamelCase    (							A__    ):
     __A	:  Dict							  =   ["""image_processor""", """tokenizer"""]
     __A	:  List[str]							  =   """BridgeTowerImageProcessor"""
     __A	:  str							  =   ("""RobertaTokenizer""", """RobertaTokenizerFast""")





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase					):
        super().__init__(_UpperCamelCase				,							_UpperCamelCase					)





     def __call__(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = True				,							_UpperCamelCase = False				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = 0				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = None				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = False				,							_UpperCamelCase = True				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        _UpperCAmelCase           =   self.tokenizer(
            text=_UpperCamelCase				,							add_special_tokens=_UpperCamelCase				,							padding=_UpperCamelCase				,							truncation=_UpperCamelCase				,							max_length=_UpperCamelCase				,							stride=_UpperCamelCase				,							pad_to_multiple_of=_UpperCamelCase				,							return_token_type_ids=_UpperCamelCase				,							return_attention_mask=_UpperCamelCase				,							return_overflowing_tokens=_UpperCamelCase				,							return_special_tokens_mask=_UpperCamelCase				,							return_offsets_mapping=_UpperCamelCase				,							return_length=_UpperCamelCase				,							verbose=_UpperCamelCase				,							return_tensors=_UpperCamelCase				,							**_UpperCamelCase				,							)
        # add pixel_values + pixel_mask
        _UpperCAmelCase           =   self.image_processor(
            _UpperCamelCase				,							return_tensors=_UpperCamelCase				,							do_normalize=_UpperCamelCase				,							do_center_crop=_UpperCamelCase				,							**_UpperCamelCase					)
        encoding.update(_UpperCamelCase					)

        return encoding





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.batch_decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							*_UpperCamelCase				,							**_UpperCamelCase					):
        return self.tokenizer.decode(*_UpperCamelCase				,							**_UpperCamelCase					)





     @property
     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   self.tokenizer.model_input_names
        _UpperCAmelCase           =   self.image_processor.model_input_names
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)