



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


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

class   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     List[str]      							=      """AutoTokenizer"""
						_snake_case			:     List[Any]      							=      ["""tokenizer"""]

						_snake_case			:     Optional[Any]      							=      {
						    """semantic_prompt""": 1,
						    """coarse_prompt""": 2,
						    """fine_prompt""": 2,
						}



						def __init__(	self	:							Tuple							,      lowerCamelCase	:							List[str]							,      lowerCamelCase	:							str=None	):






							'''simple docstring'''



							super().__init__(lowerCamelCase	)

							__lowercase		       =			speaker_embeddings



						@classmethod
						def     _snake_case	(	cls	:							Optional[Any]							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							List[Any]="speaker_embeddings_path.json"							,      **lowerCamelCase	:							Union[str, Any]	):






							'''simple docstring'''



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

							__lowercase		       =			AutoTokenizer.from_pretrained(lowerCamelCase							,      **lowerCamelCase	)

							return cls(tokenizer=lowerCamelCase							,      speaker_embeddings=lowerCamelCase	)



						def     _snake_case	(	self	:							Optional[int]							,      lowerCamelCase	:							Tuple							,      lowerCamelCase	:							str="speaker_embeddings_path.json"							,      lowerCamelCase	:							Optional[int]="speaker_embeddings"							,      lowerCamelCase	:							bool = False							,      **lowerCamelCase	:							Optional[int]							,      ):






							'''simple docstring'''



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

								__lowercase		       =			{}

								__lowercase		       =			save_directory

								for prompt_key in self.speaker_embeddings:
									if prompt_key != "repo_or_path":
										__lowercase		       =			self._load_voice_preset(lowerCamelCase	)

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

										__lowercase		       =			tmp_dict

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

							super().save_pretrained(lowerCamelCase							,      lowerCamelCase							,      **lowerCamelCase	)



						def     _snake_case	(	self	:							Any							,      lowerCamelCase	:							str = None							,      **lowerCamelCase	:							str	):






							'''simple docstring'''



							__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"							,      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."""	)

								__lowercase		       =			np.load(lowerCamelCase	)

							return voice_preset_dict



						def     _snake_case	(	self	:							Dict							,      lowerCamelCase	:							Optional[dict] = 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	:							Optional[Any]							,      lowerCamelCase	:							int=None							,      lowerCamelCase	:							Tuple=None							,      lowerCamelCase	:							Optional[Any]="pt"							,      lowerCamelCase	:							Optional[int]=256							,      lowerCamelCase	:							Any=False							,      lowerCamelCase	:							List[Any]=True							,      lowerCamelCase	:							str=False							,      **lowerCamelCase	:							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
								):
									__lowercase		       =			self._load_voice_preset(lowerCamelCase	)

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

									__lowercase		       =			np.load(lowerCamelCase	)

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

							__lowercase		       =			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:
								__lowercase		       =			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


snake_case__					:     Any    		=   logging.get_logger(__name__)

class   _A   ( _lowercase  ,						_lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      """maskformer-swin"""

						_snake_case			:     List[str]      							=      {
						    """num_attention_heads""": """num_heads""",
						    """num_hidden_layers""": """num_layers""",
						}



						def __init__(	self	:							List[str]							,      lowerCamelCase	:							Any=224							,      lowerCamelCase	:							Optional[Any]=4							,      lowerCamelCase	:							Dict=3							,      lowerCamelCase	:							Tuple=96							,      lowerCamelCase	:							str=[2, 2, 6, 2]							,      lowerCamelCase	:							Dict=[3, 6, 12, 24]							,      lowerCamelCase	:							Optional[Any]=7							,      lowerCamelCase	:							Any=4.0							,      lowerCamelCase	:							Union[str, Any]=True							,      lowerCamelCase	:							List[str]=0.0							,      lowerCamelCase	:							Optional[int]=0.0							,      lowerCamelCase	:							List[str]=0.1							,      lowerCamelCase	:							int="gelu"							,      lowerCamelCase	:							Optional[int]=False							,      lowerCamelCase	:							List[Any]=0.02							,      lowerCamelCase	:							Tuple=1e-5							,      lowerCamelCase	:							Dict=None							,      lowerCamelCase	:							Dict=None							,      **lowerCamelCase	:							int							,      ):






							'''simple docstring'''



							super().__init__(**lowerCamelCase	)

							__lowercase		       =			image_size
							__lowercase		       =			patch_size
							__lowercase		       =			num_channels
							__lowercase		       =			embed_dim
							__lowercase		       =			depths
							__lowercase		       =			len(lowerCamelCase	)
							__lowercase		       =			num_heads
							__lowercase		       =			window_size
							__lowercase		       =			mlp_ratio
							__lowercase		       =			qkv_bias
							__lowercase		       =			hidden_dropout_prob
							__lowercase		       =			attention_probs_dropout_prob
							__lowercase		       =			drop_path_rate
							__lowercase		       =			hidden_act
							__lowercase		       =			use_absolute_embeddings
							__lowercase		       =			layer_norm_eps
							__lowercase		       =			initializer_range
							# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
							# this indicates the channel dimension after the last stage of the model
							__lowercase		       =			int(embed_dim * 2 ** (len(lowerCamelCase	) - 1)	)
							__lowercase		       =			["stem"] + [f"""stage{idx}""" for idx in range(1							,      len(lowerCamelCase	) + 1	)]
							__lowercase       ,    __lowercase		       =			get_aligned_output_features_output_indices(
							    out_features=lowerCamelCase							,      out_indices=lowerCamelCase							,      stage_names=self.stage_names	)



