


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       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''AutoTokenizer'''
     SCREAMING_SNAKE_CASE__      			=			['''tokenizer''']

     SCREAMING_SNAKE_CASE__      			=			{
         '''semantic_prompt''': 1,
         '''coarse_prompt''': 2,
         '''fine_prompt''': 2,
     }





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

      _lowercase							      =speaker_embeddings





     @classmethod
     def      __A					(cls		,     UpperCAmelCase		,     UpperCAmelCase="speaker_embeddings_path.json"		,     **UpperCAmelCase       )     ->   Union[str, Any]:

      if speaker_embeddings_dict_path is not None:
       _lowercase							      =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`."       )
        _lowercase							      =None
       else:
        with open(UpperCAmelCase       ) as speaker_embeddings_json:
         _lowercase							      =json.load(UpperCAmelCase       )
      else:
       _lowercase							      =None

      _lowercase							      =AutoTokenizer.from_pretrained(UpperCAmelCase		,     **UpperCAmelCase       )

      return cls(tokenizer=UpperCAmelCase		,     speaker_embeddings=UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase="speaker_embeddings_path.json"		,     UpperCAmelCase="speaker_embeddings"		,     UpperCAmelCase = False		,     **UpperCAmelCase		,     )     ->   int:
      if self.speaker_embeddings is not None:
       os.makedirs(os.path.join(UpperCAmelCase		,     UpperCAmelCase		,     '''v2'''       )		,     exist_ok=UpperCAmelCase       )

       _lowercase							      ={}

       _lowercase							      =save_directory

       for prompt_key in self.speaker_embeddings:
        if prompt_key != "repo_or_path":
         _lowercase							      =self._load_voice_preset(UpperCAmelCase       )

         _lowercase							      ={}
         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		,     )
          _lowercase							      =os.path.join(UpperCAmelCase		,     f"{prompt_key}_{key}.npy"       )

         _lowercase							      =tmp_dict

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

      super().save_pretrained(UpperCAmelCase		,     UpperCAmelCase		,     **UpperCAmelCase       )





     def      __A					(self		,     UpperCAmelCase = None		,     **UpperCAmelCase       )     ->   int:
      _lowercase							      =self.speaker_embeddings[voice_preset]

      _lowercase							      ={}
      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}]."       )

       _lowercase							      =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."       )

       _lowercase							      =np.load(UpperCAmelCase       )

      return voice_preset_dict





     def      __A					(self		,     UpperCAmelCase = None       )     ->   List[Any]:
      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=2_5_6		,     UpperCAmelCase=False		,     UpperCAmelCase=True		,     UpperCAmelCase=False		,     **UpperCAmelCase		,     )     ->   List[Any]:
      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
       ):
        _lowercase							      =self._load_voice_preset(UpperCAmelCase       )

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

        _lowercase							      =np.load(UpperCAmelCase       )

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

      _lowercase							      =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:
       _lowercase							      =voice_preset

      return encoded_text





from dataclasses import asdict, dataclass
from typing import Optional

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


UpperCAmelCase__   	=							logging.get_logger(__name__)

# TODO Update this
UpperCAmelCase__   	=							{
    '''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
    # See all ESM models at https://huggingface.co/models?filter=esm
}







class       lowerCamelCase__   (       lowerCAmelCase):
     SCREAMING_SNAKE_CASE__      			=			'''esm'''





     def __init__(self		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=7_6_8		,     UpperCAmelCase=1_2		,     UpperCAmelCase=1_2		,     UpperCAmelCase=3_0_7_2		,     UpperCAmelCase=0.1		,     UpperCAmelCase=0.1		,     UpperCAmelCase=1_0_2_6		,     UpperCAmelCase=0.02		,     UpperCAmelCase=1e-12		,     UpperCAmelCase="absolute"		,     UpperCAmelCase=True		,     UpperCAmelCase=None		,     UpperCAmelCase=False		,     UpperCAmelCase=False		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     **UpperCAmelCase		,     )     ->   Tuple:
      super().__init__(pad_token_id=UpperCAmelCase		,     mask_token_id=UpperCAmelCase		,     **UpperCAmelCase       )

      _lowercase							      =vocab_size
      _lowercase							      =hidden_size
      _lowercase							      =num_hidden_layers
      _lowercase							      =num_attention_heads
      _lowercase							      =intermediate_size
      _lowercase							      =hidden_dropout_prob
      _lowercase							      =attention_probs_dropout_prob
      _lowercase							      =max_position_embeddings
      _lowercase							      =initializer_range
      _lowercase							      =layer_norm_eps
      _lowercase							      =position_embedding_type
      _lowercase							      =use_cache
      _lowercase							      =emb_layer_norm_before
      _lowercase							      =token_dropout
      _lowercase							      =is_folding_model
      if is_folding_model:
       if esmfold_config is None:
        logger.info('''No esmfold_config supplied for folding model, using default values.'''       )
        _lowercase							      =EsmFoldConfig()
       elif isinstance(UpperCAmelCase		,     UpperCAmelCase       ):
        _lowercase							      =EsmFoldConfig(**UpperCAmelCase       )
       _lowercase							      =esmfold_config
       if vocab_list is None:
        logger.warning('''No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!'''       )
        _lowercase							      =get_default_vocab_list()
       else:
        _lowercase							      =vocab_list
      else:
       _lowercase							      =None
       _lowercase							      =None
      if self.esmfold_config is not None and getattr(self.esmfold_config		,     '''use_esm_attn_map'''		,     UpperCAmelCase       ):
       raise ValueError('''The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!'''       )





     def      __A					(self       )     ->   List[str]:
      _lowercase							      =super().to_dict()
      if isinstance(self.esmfold_config		,     UpperCAmelCase       ):
       _lowercase							      =self.esmfold_config.to_dict()
      return output







@dataclass
class       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			None
     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			0

     SCREAMING_SNAKE_CASE__      			=			True
     SCREAMING_SNAKE_CASE__      			=			False

     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			None





     def      __A					(self       )     ->   Union[str, Any]:
      if self.trunk is None:
       _lowercase							      =TrunkConfig()
      elif isinstance(self.trunk		,     UpperCAmelCase       ):
       _lowercase							      =TrunkConfig(**self.trunk       )





     def      __A					(self       )     ->   Tuple:
      _lowercase							      =asdict(self       )
      _lowercase							      =self.trunk.to_dict()
      return output







@dataclass
class       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			48
     SCREAMING_SNAKE_CASE__      			=			1024
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			32
     SCREAMING_SNAKE_CASE__      			=			32
     SCREAMING_SNAKE_CASE__      			=			32
     SCREAMING_SNAKE_CASE__      			=			0
     SCREAMING_SNAKE_CASE__      			=			0
     SCREAMING_SNAKE_CASE__      			=			False
     SCREAMING_SNAKE_CASE__      			=			4
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			None





     def      __A					(self       )     ->   List[str]:
      if self.structure_module is None:
       _lowercase							      =StructureModuleConfig()
      elif isinstance(self.structure_module		,     UpperCAmelCase       ):
       _lowercase							      =StructureModuleConfig(**self.structure_module       )

      if self.max_recycles <= 0:
       raise ValueError(f"`max_recycles` should be positive, got {self.max_recycles}."       )
      if self.sequence_state_dim % self.sequence_state_dim != 0:
       raise ValueError(
           '''`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got'''
           f" {self.sequence_state_dim} and {self.sequence_state_dim}."       )
      if self.pairwise_state_dim % self.pairwise_state_dim != 0:
       raise ValueError(
           '''`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got'''
           f" {self.pairwise_state_dim} and {self.pairwise_state_dim}."       )

      _lowercase							      =self.sequence_state_dim // self.sequence_head_width
      _lowercase							      =self.pairwise_state_dim // self.pairwise_head_width

      if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
       raise ValueError(
           '''`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got'''
           f" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}."       )
      if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
       raise ValueError(
           '''`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got'''
           f" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}."       )
      if self.pairwise_state_dim % 2 != 0:
       raise ValueError(f"`pairwise_state_dim` should be even, got {self.pairwise_state_dim}."       )

      if self.dropout >= 0.4:
       raise ValueError(f"`dropout` should not be greater than 0.4, got {self.dropout}."       )





     def      __A					(self       )     ->   Dict:
      _lowercase							      =asdict(self       )
      _lowercase							      =self.structure_module.to_dict()
      return output







@dataclass
class       lowerCamelCase__   :
     SCREAMING_SNAKE_CASE__      			=			384
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			16
     SCREAMING_SNAKE_CASE__      			=			128
     SCREAMING_SNAKE_CASE__      			=			12
     SCREAMING_SNAKE_CASE__      			=			4
     SCREAMING_SNAKE_CASE__      			=			8
     SCREAMING_SNAKE_CASE__      			=			0.1
     SCREAMING_SNAKE_CASE__      			=			8
     SCREAMING_SNAKE_CASE__      			=			1
     SCREAMING_SNAKE_CASE__      			=			2
     SCREAMING_SNAKE_CASE__      			=			7
     SCREAMING_SNAKE_CASE__      			=			10
     SCREAMING_SNAKE_CASE__      			=			1E-8
     SCREAMING_SNAKE_CASE__      			=			1E5





     def      __A					(self       )     ->   List[Any]:
      return asdict(self       )



def 				UpperCAmelCase_					(							)     ->			Tuple:







 """simple docstring"""


 return (
     "<cls>",
     "<pad>",
     "<eos>",
     "<unk>",
     "L",
     "A",
     "G",
     "V",
     "S",
     "E",
     "R",
     "T",
     "I",
     "D",
     "P",
     "K",
     "Q",
     "N",
     "F",
     "Y",
     "M",
     "H",
     "W",
     "C",
     "X",
     "B",
     "U",
     "Z",
     "O",
     ".",
     "-",
     "<null_1>",
     "<mask>",
 )

