
"""simple docstring"""


import copy
from typing import Dict, List, Optional

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


_lowercase							   =				{
    '''facebook/mask2former-swin-small-coco-instance''': (
        '''https://huggingface.co/facebook/mask2former-swin-small-coco-instance/blob/main/config.json'''
    )
    # See all Mask2Former models at https://huggingface.co/models?filter=mask2former
}

_lowercase							   =				logging.get_logger(__name__)







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    List[Any]      =    '''mask2former'''
						_lowerCamelCase:    Dict      =    ['''swin''']
						_lowerCamelCase:    Union[str, Any]      =    {'''hidden_size''': '''hidden_dim'''}


						def __init__( self     :      Any  ,A_     :      Optional[Dict] = None  ,A_     :      int = 256  ,A_     :      int = 256  ,A_     :      int = 256  ,A_     :      int = 1024  ,A_     :      str = "relu"  ,A_     :      int = 6  ,A_     :      int = 10  ,A_     :      int = 8  ,A_     :      float = 0.0  ,A_     :      int = 2048  ,A_     :      bool = False  ,A_     :      bool = False  ,A_     :      int = 4  ,A_     :      int = 255  ,A_     :      int = 100  ,A_     :      float = 0.1  ,A_     :      float = 2.0  ,A_     :      float = 5.0  ,A_     :      float = 5.0  ,A_     :      int = 1_2544  ,A_     :      float = 3.0  ,A_     :      float = 0.75  ,A_     :      float = 0.02  ,A_     :      float = 1.0  ,A_     :      bool = True  ,A_     :      List[int] = [4, 8, 16, 32]  ,A_     :      bool = None  ,**A_     :      Optional[int]  ,)     ->   Optional[int]:
							if backbone_config is None:
								logger.info('`backbone_config` is `None`. Initializing the config with the default `Swin` backbone.'      )
								A						   =   CONFIG_MAPPING['swin'](
								    image_size=224  ,in_channels=3  ,patch_size=4  ,embed_dim=96  ,depths=[2, 2, 18, 2]  ,num_heads=[3, 6, 12, 24]  ,window_size=7  ,drop_path_rate=0.3  ,use_absolute_embeddings=A_  ,out_features=['stage1', 'stage2', 'stage3', 'stage4']  ,)

							if isinstance(A_  ,A_      ):
								A						   =   backbone_config.pop('model_type'      )
								A						   =   CONFIG_MAPPING[backbone_model_type]
								A						   =   config_class.from_dict(A_      )

							# verify that the backbone is supported
							if backbone_config.model_type not in self.backbones_supported:
								logger.warning_once(
								    F'Backbone {backbone_config.model_type} is not a supported model and may not be compatible with Mask2Former. '
								    F'Supported model types: {",".join(self.backbones_supported      )}'      )

							A						   =   backbone_config
							A						   =   feature_size
							A						   =   mask_feature_size
							A						   =   hidden_dim
							A						   =   encoder_feedforward_dim
							A						   =   activation_function
							A						   =   encoder_layers
							A						   =   decoder_layers
							A						   =   num_attention_heads
							A						   =   dropout
							A						   =   dim_feedforward
							A						   =   pre_norm
							A						   =   enforce_input_projection
							A						   =   common_stride
							A						   =   ignore_value
							A						   =   num_queries
							A						   =   no_object_weight
							A						   =   class_weight
							A						   =   mask_weight
							A						   =   dice_weight
							A						   =   train_num_points
							A						   =   oversample_ratio
							A						   =   importance_sample_ratio
							A						   =   init_std
							A						   =   init_xavier_std
							A						   =   use_auxiliary_loss
							A						   =   feature_strides
							A						   =   output_auxiliary_logits
							A						   =   decoder_layers

							super().__init__(**A_      )


						@classmethod
						def 						_SCREAMING_SNAKE_CASE      ( cls     :      List[str]  ,A_     :      PretrainedConfig  ,**A_     :      Dict      )     ->   Union[str, Any]:
							return cls(
							    backbone_config=A_  ,**A_  ,)




						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]      )     ->   Dict[str, any]:
							A						   =   copy.deepcopy(self.__dict__      )
							A						   =   self.backbone_config.to_dict()
							A						   =   self.__class__.model_type
							return output

"""simple docstring"""


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


_lowercase							   =				logging.get_logger(__name__)

_lowercase							   =				{'''vocab_file''': '''spm_char.model'''}

_lowercase							   =				{
    '''vocab_file''': {
        '''microsoft/speecht5_asr''': '''https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model''',
        '''microsoft/speecht5_tts''': '''https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model''',
        '''microsoft/speecht5_vc''': '''https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model''',
    }
}

_lowercase							   =				{
    '''microsoft/speecht5_asr''': 10_24,
    '''microsoft/speecht5_tts''': 10_24,
    '''microsoft/speecht5_vc''': 10_24,
}







class 				lowerCAmelCase_				(      _lowercase    ):



						'''simple docstring'''


						_lowerCamelCase:    Optional[Any]      =    VOCAB_FILES_NAMES
						_lowerCamelCase:    List[Any]      =    PRETRAINED_VOCAB_FILES_MAP
						_lowerCamelCase:    str      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						_lowerCamelCase:    Tuple      =    ['''input_ids''', '''attention_mask''']


						def __init__( self     :      List[str]  ,A_     :      int  ,A_     :      List[str]="<s>"  ,A_     :      Optional[Any]="</s>"  ,A_     :      Optional[Any]="<unk>"  ,A_     :      str="<pad>"  ,A_     :      Optional[Dict[str, Any]] = None  ,**A_     :      List[str]  ,)     ->   None:
							A						   =   {} if sp_model_kwargs is None else sp_model_kwargs

							super().__init__(
							    bos_token=A_  ,eos_token=A_  ,unk_token=A_  ,pad_token=A_  ,sp_model_kwargs=self.sp_model_kwargs  ,**A_  ,)

							A						   =   vocab_file

							A						   =   spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(A_      )


						@property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   str:
							return self.sp_model.get_piece_size()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[Any]:
							A						   =   {self.convert_ids_to_tokens(A_      ): i for i in range(self.vocab_size      )}
							vocab.update(self.added_tokens_encoder      )
							return vocab


						def __getstate__( self     :      str      )     ->   Any:
							A						   =   self.__dict__.copy()
							A						   =   None
							return state


						def __setstate__( self     :      Optional[int]  ,A_     :      str      )     ->   Tuple:
							A						   =   d

							# for backward compatibility
							if not hasattr(self  ,'sp_model_kwargs'      ):
								A						   =   {}

							A						   =   spm.SentencePieceProcessor(**self.sp_model_kwargs      )
							self.sp_model.Load(self.vocab_file      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      str      )     ->   List[str]:
							return self.sp_model.encode(A_  ,out_type=A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[str]  ,A_     :      Union[str, Any]      )     ->   Union[str, Any]:
							return self.sp_model.piece_to_id(A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      Dict      )     ->   List[Any]:
							A						   =   self.sp_model.IdToPiece(A_      )
							return token


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple  ,A_     :      Optional[Any]      )     ->   List[str]:
							A						   =   []
							A						   =   ''
							for token in tokens:
								# make sure that special tokens are not decoded using sentencepiece model
								if token in self.all_special_tokens:
									out_string += self.sp_model.decode(A_      ) + token
									A						   =   []
								else:
									current_sub_tokens.append(A_      )
							out_string += self.sp_model.decode(A_      )
							return out_string.strip()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Dict  ,A_     :      Optional[int]=None      )     ->   List[int]:
							if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
							# We don't expect to process pairs, but leave the pair logic for API consistency
							return token_ids_a + token_ids_a + [self.eos_token_id]


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      List[int]  ,A_     :      Optional[List[int]] = None  ,A_     :      bool = False      )     ->   List[int]:
							if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=A_  ,token_ids_a=A_  ,already_has_special_tokens=A_      )

							A						   =   [1]
							if token_ids_a is None:
								return ([0] * len(A_      )) + suffix_ones
							return ([0] * len(A_      )) + ([0] * len(A_      )) + suffix_ones




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      str  ,A_     :      Optional[str] = None      )     ->   Tuple[str]:
							if not os.path.isdir(A_      ):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'      )
								return
							A						   =   os.path.join(
							    A_  ,(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

							if os.path.abspath(self.vocab_file      ) != os.path.abspath(A_      ) and os.path.isfile(self.vocab_file      ):
								copyfile(self.vocab_file  ,A_      )
							elif not os.path.isfile(self.vocab_file      ):
								with open(A_  ,'wb'      ) as fi:
									A						   =   self.sp_model.serialized_model_proto()
									fi.write(A_      )

							return (out_vocab_file,)