






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_		:     Union[str, Any]        =     logging.get_logger(__name__)



class 		_lowercase		(       UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''AutoTokenizer'''
   _UpperCAmelCase     	=			['''tokenizer''']

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

   def __init__(      self			:       List[str]							,   __lowerCAmelCase			:       int							,   __lowerCAmelCase			:       Optional[Any]=None							)       ->				Union[str, Any]:


    """simple docstring"""






    super().__init__(__lowerCAmelCase							)

    a						=		speaker_embeddings

   @classmethod
   def 		A							(      cls			:       Optional[Any]							,   __lowerCAmelCase			:       Union[str, Any]							,   __lowerCAmelCase			:       Union[str, Any]="speaker_embeddings_path.json"							,   **__lowerCAmelCase			:       List[str]							)       ->				str:


    """simple docstring"""






    if speaker_embeddings_dict_path is not None:
     a						=		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						=		None
     else:
      with open(__lowerCAmelCase							) as speaker_embeddings_json:
       a						=		json.load(__lowerCAmelCase							)
    else:
     a						=		None

    a						=		AutoTokenizer.from_pretrained(__lowerCAmelCase							,   **__lowerCAmelCase							)

    return cls(tokenizer=__lowerCAmelCase							,   speaker_embeddings=__lowerCAmelCase							)

   def 		A							(      self			:       Tuple							,   __lowerCAmelCase			:       List[str]							,   __lowerCAmelCase			:       Union[str, Any]="speaker_embeddings_path.json"							,   __lowerCAmelCase			:       str="speaker_embeddings"							,   __lowerCAmelCase			:       bool = False							,   **__lowerCAmelCase			:       Dict							,   )       ->				Optional[Any]:


    """simple docstring"""






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

     a						=		{}

     a						=		save_directory

     for prompt_key in self.speaker_embeddings:
      if prompt_key != "repo_or_path":
       a						=		self._load_voice_preset(__lowerCAmelCase							)

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

       a						=		tmp_dict

     with open(os.path.join(__lowerCAmelCase							,   __lowerCAmelCase							)							,   "w"							) as fp:
      json.dump(__lowerCAmelCase							,   __lowerCAmelCase							)

    super().save_pretrained(__lowerCAmelCase							,   __lowerCAmelCase							,   **__lowerCAmelCase							)

   def 		A							(      self			:       List[Any]							,   __lowerCAmelCase			:       str = None							,   **__lowerCAmelCase			:       Union[str, Any]							)       ->				Optional[int]:


    """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"							,   __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						=		np.load(__lowerCAmelCase							)

    return voice_preset_dict

   def 		A							(      self			:       Optional[int]							,   __lowerCAmelCase			:       Optional[dict] = None							)       ->				Dict:


    """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			:       List[Any]							,   __lowerCAmelCase			:       str=None							,   __lowerCAmelCase			:       Union[str, Any]=None							,   __lowerCAmelCase			:       Union[str, Any]="pt"							,   __lowerCAmelCase			:       List[Any]=256							,   __lowerCAmelCase			:       List[str]=False							,   __lowerCAmelCase			:       Any=True							,   __lowerCAmelCase			:       List[str]=False							,   **__lowerCAmelCase			:       List[Any]							,   )       ->				Tuple:


    """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						=		self._load_voice_preset(__lowerCAmelCase							)

     else:
      if isinstance(__lowerCAmelCase							,   __lowerCAmelCase							) and not voice_preset.endswith(".npz"							):
       a						=		voice_preset + ".npz"

      a						=		np.load(__lowerCAmelCase							)

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

    a						=		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						=		voice_preset

    return encoded_text






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


A_		:     int        =     logging.get_logger(__name__)

A_		:     str        =     {
    '''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}



class 		_lowercase		(       UpperCAmelCase__,   UpperCAmelCase__	):
   _UpperCAmelCase     	=			'''focalnet'''

   def __init__(      self			:       int							,   __lowerCAmelCase			:       Optional[Any]=224							,   __lowerCAmelCase			:       Any=4							,   __lowerCAmelCase			:       Any=3							,   __lowerCAmelCase			:       Tuple=96							,   __lowerCAmelCase			:       Dict=False							,   __lowerCAmelCase			:       Optional[int]=[192, 384, 768, 768]							,   __lowerCAmelCase			:       Union[str, Any]=[2, 2, 6, 2]							,   __lowerCAmelCase			:       Optional[int]=[2, 2, 2, 2]							,   __lowerCAmelCase			:       Union[str, Any]=[3, 3, 3, 3]							,   __lowerCAmelCase			:       str="gelu"							,   __lowerCAmelCase			:       Any=4.0							,   __lowerCAmelCase			:       Optional[int]=0.0							,   __lowerCAmelCase			:       Tuple=0.1							,   __lowerCAmelCase			:       str=False							,   __lowerCAmelCase			:       Optional[int]=1E-4							,   __lowerCAmelCase			:       Optional[Any]=False							,   __lowerCAmelCase			:       Union[str, Any]=False							,   __lowerCAmelCase			:       str=False							,   __lowerCAmelCase			:       Any=0.0_2							,   __lowerCAmelCase			:       str=1E-5							,   __lowerCAmelCase			:       Optional[Any]=32							,   __lowerCAmelCase			:       Dict=None							,   __lowerCAmelCase			:       str=None							,   **__lowerCAmelCase			:       Any							,   )       ->				List[str]:


    """simple docstring"""






    super().__init__(**__lowerCAmelCase							)

    a						=		image_size
    a						=		patch_size
    a						=		num_channels
    a						=		embed_dim
    a						=		use_conv_embed
    a						=		hidden_sizes
    a						=		depths
    a						=		focal_levels
    a						=		focal_windows
    a						=		hidden_act
    a						=		mlp_ratio
    a						=		hidden_dropout_prob
    a						=		drop_path_rate
    a						=		use_layerscale
    a						=		layerscale_value
    a						=		use_post_layernorm
    a						=		use_post_layernorm_in_modulation
    a						=		normalize_modulator
    a						=		initializer_range
    a						=		layer_norm_eps
    a						=		encoder_stride
    a						=		["stem"] + [f"""stage{idx}""" for idx in range(1							,   len(self.depths							) + 1							)]
    a		,  a						=		get_aligned_output_features_output_indices(
        out_features=__lowerCAmelCase							,   out_indices=__lowerCAmelCase							,   stage_names=self.stage_names							)

