



'''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


lowercase_									=    logging.get_logger(__name__)






class __A     (    A		):



 '''simple docstring'''







 __lowerCamelCase       :			int		=			'AutoTokenizer'
 __lowerCamelCase       :			Tuple		=			['tokenizer']

 __lowerCamelCase       :			List[str]		=			{
     'semantic_prompt': 1,
     'coarse_prompt': 2,
     'fine_prompt': 2,
 }



 def __init__(self					,  A					,  A=None			)       ->	str:


      """simple docstring"""



      super().__init__(A			)

      _a           =    speaker_embeddings



 @classmethod
 def    a__    (cls					,  A					,  A="speaker_embeddings_path.json"					,  **A			)       ->	Optional[int]:


      """simple docstring"""



      if speaker_embeddings_dict_path is not None:
           _a           =    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
                    , 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           =    None
           else:
                with open(A			) as speaker_embeddings_json:
                     _a           =    json.load(A			)
      else:
           _a           =    None

      _a           =    AutoTokenizer.from_pretrained(A					,  **A			)

      return cls(tokenizer=A					,  speaker_embeddings=A			)



 def    a__    (self					,  A					,  A="speaker_embeddings_path.json"					,  A="speaker_embeddings"					,  A = False					,  **A					,  )       ->	int:


      """simple docstring"""



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

           _a           =    {}

           _a           =    save_directory

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

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

                     _a           =    tmp_dict

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

      super().save_pretrained(A					,  A					,  **A			)



 def    a__    (self					,  A = None					,  **A			)       ->	Dict:


      """simple docstring"""



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

           _a           =    np.load(A			)

      return voice_preset_dict



 def    a__    (self					,  A = None			)       ->	int:


      """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					,  A=None					,  A=None					,  A="pt"					,  A=256					,  A=False					,  A=True					,  A=False					,  **A					,  )       ->	Optional[int]:


      """simple docstring"""



      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
           ):
                _a           =    self._load_voice_preset(A			)

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

                _a           =    np.load(A			)

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

      _a           =    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:
           _a           =    voice_preset

      return encoded_text











import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.generation import DisjunctiveConstraint



@require_torch
class 				_UpperCamelCase      (unittest.TestCase      ):


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           # For consistency across different places the DisjunctiveConstraint is called,
           # dc.token_ids is a list of integers. It is also initialized only by integers.

           __lowerCAmelCase             =       [[1, 2, 4], [1, 2, 3, 4]]
           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)
           self.assertTrue(isinstance(dc.token_ids       ,    __UpperCamelCase						)						)

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]						)						)

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint([torch.LongTensor([1, 2, 4]						), torch.LongTensor([1, 2, 3, 4, 5]						)]						)


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           # We can't have constraints that are complete subsets of another. This leads to a preverse
           # interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
           # It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
           # fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
           # will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
           __lowerCAmelCase             =       [[1, 2], [1, 2, 3, 4]]

           with self.assertRaises(__UpperCamelCase						):
                DisjunctiveConstraint(__UpperCamelCase						)  # fails here


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase             =       [[1, 2, 3], [1, 2, 4]]

           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           __lowerCAmelCase             =       stepped is True and completed is False and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           __lowerCAmelCase             =       stepped is True and completed is False and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(3						)
           __lowerCAmelCase             =       stepped is True and completed is True and reset is False
           self.assertTrue(__UpperCamelCase						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 3]						)


      def 			__UpperCAmelCase		(						self						)->						int:
           __lowerCAmelCase             =       [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

           __lowerCAmelCase             =       DisjunctiveConstraint(__UpperCamelCase						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(4						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.current_seq == [1, 2, 4]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(5						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.current_seq == [1, 2, 4, 5]						)

           dc.reset()

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(1						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.remaining() == 3						)
           self.assertTrue(dc.current_seq == [1]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(2						)
           self.assertTrue(not dc.completed						)
           self.assertTrue(dc.remaining() == 2						)
           self.assertTrue(dc.current_seq == [1, 2]						)

           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       dc.update(5						)
           self.assertTrue(dc.completed						)  # Completed!
           self.assertTrue(dc.remaining() == 0						)
           self.assertTrue(dc.current_seq == [1, 2, 5]						)






