



"""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 SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					'AutoTokenizer'
						_lowerCAmelCase							=					['tokenizer']

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



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





													'''simple docstring'''

													super().__init__(A_							)

													__a		:		List[Any]									=       speaker_embeddings



						@classmethod
						def     lowerCAmelCase__(cls						,     _lowercase						,     _lowercase="speaker_embeddings_path.json"						,     **_lowercase							):





													'''simple docstring'''

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

													__a		:		Optional[int]									=       AutoTokenizer.from_pretrained(A_						,     **A_							)

													return cls(tokenizer=A_						,     speaker_embeddings=A_							)



						def     lowerCAmelCase__(self						,     _lowercase						,     _lowercase="speaker_embeddings_path.json"						,     _lowercase="speaker_embeddings"						,     _lowercase = False						,     **_lowercase						,     ):





													'''simple docstring'''

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

																				__a		:		Dict									=       {}

																				__a		:		int									=       save_directory

																				for prompt_key in self.speaker_embeddings:
																											if prompt_key != "repo_or_path":
																																		__a		:		Any									=       self._load_voice_preset(A_							)

																																		__a		:		List[Any]									=       {}
																																		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		:		Any									=       os.path.join(A_						,     F'''{prompt_key}_{key}.npy'''							)

																																		__a		:		List[Any]									=       tmp_dict

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

													super().save_pretrained(A_						,     A_						,     **A_							)



						def     lowerCAmelCase__(self						,     _lowercase = None						,     **_lowercase							):





													'''simple docstring'''

													__a		:		Optional[int]									=       self.speaker_embeddings[voice_preset]

													__a		:		Dict									=       {}
													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		:		int									=       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		:		Dict									=       np.load(A_							)

													return voice_preset_dict



						def     lowerCAmelCase__(self						,     _lowercase = 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						,     _lowercase=None						,     _lowercase=None						,     _lowercase="pt"						,     _lowercase=256						,     _lowercase=False						,     _lowercase=True						,     _lowercase=False						,     **_lowercase						,     ):





													'''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		:		Union[str, Any]									=       self._load_voice_preset(A_							)

																				else:
																											if isinstance(A_						,     A_							) and not voice_preset.endswith(""".npz"""							):
																																		__a		:		Any									=       voice_preset + ".npz"

																											__a		:		Optional[int]									=       np.load(A_							)

													if voice_preset is not None:
																				self._validate_voice_preset_dict(A_						,     **A_							)
																				__a		:		Optional[int]									=       BatchFeature(data=A_						,     tensor_type=A_							)

													__a		:		Tuple									=       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		:		Tuple									=       voice_preset

													return encoded_text






"""simple docstring"""

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


lowercase__			   =  logging.get_logger(__name__)

lowercase__			   =  {
    "sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class SCREAMING_SNAKE_CASE__					(		__snake_case      ):
						_lowerCAmelCase							=					"vit_msn"



						def __init__(self						,     _lowercase=768						,     _lowercase=12						,     _lowercase=12						,     _lowercase=3072						,     _lowercase="gelu"						,     _lowercase=0.0						,     _lowercase=0.0						,     _lowercase=0.02						,     _lowercase=1e-06						,     _lowercase=224						,     _lowercase=16						,     _lowercase=3						,     _lowercase=True						,     **_lowercase						,     ):





													'''simple docstring'''

													super().__init__(**_lowercase							)

													__a		:		int									=       hidden_size
													__a		:		str									=       num_hidden_layers
													__a		:		str									=       num_attention_heads
													__a		:		Optional[Any]									=       intermediate_size
													__a		:		Union[str, Any]									=       hidden_act
													__a		:		Tuple									=       hidden_dropout_prob
													__a		:		Any									=       attention_probs_dropout_prob
													__a		:		List[Any]									=       initializer_range
													__a		:		Any									=       layer_norm_eps
													__a		:		Dict									=       image_size
													__a		:		List[Any]									=       patch_size
													__a		:		Dict									=       num_channels
													__a		:		Optional[Any]									=       qkv_bias

