





"""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


a_			=      logging.get_logger(__name__)






class 		__snake_case (		SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""AutoTokenizer"""
 _lowerCamelCase				=					["""tokenizer"""]

 _lowerCamelCase				=					{
     """semantic_prompt""": 1,
     """coarse_prompt""": 2,
     """fine_prompt""": 2,
 }

 def __init__(   self ,						__lowerCamelCase ,						__lowerCamelCase=None  ):



     '''simple docstring'''




     super().__init__(__lowerCamelCase  )

     __A   :						Dict									=					speaker_embeddings

 @classmethod
 def  UpperCamelCase__(   cls ,						__lowerCamelCase ,						__lowerCamelCase="speaker_embeddings_path.json" ,						**__lowerCamelCase  ):



     '''simple docstring'''




     if speaker_embeddings_dict_path is not None:
         __A   :						Optional[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   :						Optional[int]									=					None
         else:
             with open(__lowerCamelCase  ) as speaker_embeddings_json:
                 __A   :						int									=					json.load(__lowerCamelCase  )
     else:
         __A   :						Optional[int]									=					None

     __A   :						List[str]									=					AutoTokenizer.from_pretrained(__lowerCamelCase ,						**__lowerCamelCase  )

     return cls(tokenizer=__lowerCamelCase ,						speaker_embeddings=__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase ,						__lowerCamelCase="speaker_embeddings_path.json" ,						__lowerCamelCase="speaker_embeddings" ,						__lowerCamelCase = False ,						**__lowerCamelCase ,						):



     '''simple docstring'''




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

         __A   :						List[str]									=					{}

         __A   :						List[Any]									=					save_directory

         for prompt_key in self.speaker_embeddings:
             if prompt_key != "repo_or_path":
                 __A   :						Tuple									=					self._load_voice_preset(__lowerCamelCase  )

                 __A   :						Tuple									=					{}
                 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   :						Union[str, Any]									=					os.path.join(__lowerCamelCase ,						F"""{prompt_key}_{key}.npy"""  )

                 __A   :						Tuple									=					tmp_dict

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

     super().save_pretrained(__lowerCamelCase ,						__lowerCamelCase ,						**__lowerCamelCase  )

 def  UpperCamelCase__(   self ,						__lowerCamelCase = None ,						**__lowerCamelCase  ):



     '''simple docstring'''




     __A   :						List[str]									=					self.speaker_embeddings[voice_preset]

     __A   :						Tuple									=					{}
     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   :						Union[str, Any]									=					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   :						int									=					np.load(__lowerCamelCase  )

     return voice_preset_dict

 def  UpperCamelCase__(   self ,						__lowerCamelCase = 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 ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						__lowerCamelCase="pt" ,						__lowerCamelCase=256 ,						__lowerCamelCase=False ,						__lowerCamelCase=True ,						__lowerCamelCase=False ,						**__lowerCamelCase ,						):



     '''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   :						Any									=					self._load_voice_preset(__lowerCamelCase  )

         else:
             if isinstance(__lowerCamelCase ,						__lowerCamelCase  ) and not voice_preset.endswith('''.npz'''  ):
                 __A   :						int									=					voice_preset + '''.npz'''

             __A   :						Optional[int]									=					np.load(__lowerCamelCase  )

     if voice_preset is not None:
         self._validate_voice_preset_dict(__lowerCamelCase ,						**__lowerCamelCase  )
         __A   :						List[Any]									=					BatchFeature(data=__lowerCamelCase ,						tensor_type=__lowerCamelCase  )

     __A   :						Tuple									=					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   :						Union[str, Any]									=					voice_preset

     return encoded_text











"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


a_			=      logging.get_logger(__name__)

a_			=      {
    """shi-labs/dinat-mini-in1k-224""": """https://huggingface.co/shi-labs/dinat-mini-in1k-224/resolve/main/config.json""",
    # See all Dinat models at https://huggingface.co/models?filter=dinat
}






class 		__snake_case (		SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__					):





 """simple docstring"""


 _lowerCamelCase				=					"""dinat"""

 _lowerCamelCase				=					{
     """num_attention_heads""": """num_heads""",
     """num_hidden_layers""": """num_layers""",
 }

 def __init__(   self ,						__lowerCamelCase=4 ,						__lowerCamelCase=3 ,						__lowerCamelCase=64 ,						__lowerCamelCase=[3, 4, 6, 5] ,						__lowerCamelCase=[2, 4, 8, 16] ,						__lowerCamelCase=7 ,						__lowerCamelCase=[[1, 8, 1], [1, 4, 1, 4], [1, 2, 1, 2, 1, 2], [1, 1, 1, 1, 1]] ,						__lowerCamelCase=3.0 ,						__lowerCamelCase=True ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=1e-5 ,						__lowerCamelCase=0.0 ,						__lowerCamelCase=None ,						__lowerCamelCase=None ,						**__lowerCamelCase ,						):



     '''simple docstring'''




     super().__init__(**__lowerCamelCase  )

     __A   :						Dict									=					patch_size
     __A   :						Union[str, Any]									=					num_channels
     __A   :						str									=					embed_dim
     __A   :						Optional[Any]									=					depths
     __A   :						int									=					len(__lowerCamelCase  )
     __A   :						Union[str, Any]									=					num_heads
     __A   :						Tuple									=					kernel_size
     __A   :						Optional[int]									=					dilations
     __A   :						Tuple									=					mlp_ratio
     __A   :						Optional[int]									=					qkv_bias
     __A   :						int									=					hidden_dropout_prob
     __A   :						Dict									=					attention_probs_dropout_prob
     __A   :						int									=					drop_path_rate
     __A   :						Dict									=					hidden_act
     __A   :						Any									=					layer_norm_eps
     __A   :						Tuple									=					initializer_range
     # we set the hidden_size attribute in order to make Dinat work with VisionEncoderDecoderModel
     # this indicates the channel dimension after the last stage of the model
     __A   :						str									=					int(embed_dim * 2 ** (len(__lowerCamelCase  ) - 1)  )
     __A   :						List[Any]									=					layer_scale_init_value
     __A   :						Any									=					['''stem'''] + [F"""stage{idx}""" for idx in range(1 ,						len(__lowerCamelCase  ) + 1  )]
     __A			,	__A   :						Union[str, Any]									=					get_aligned_output_features_output_indices(
         out_features=__lowerCamelCase ,						out_indices=__lowerCamelCase ,						stage_names=self.stage_names  )




