
"""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


_a														=		logging.get_logger(__name__)

_a														=		{"""vocab_file""": """spm_char.model"""}

_a														=		{
    """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""",
    }
}

_a														=		{
    """microsoft/speecht5_asr""": 1024,
    """microsoft/speecht5_tts""": 1024,
    """microsoft/speecht5_vc""": 1024,
}






class    _UpperCAmelCase(   lowerCamelCase     ):
				lowercase__      =				VOCAB_FILES_NAMES
				lowercase__      =				PRETRAINED_VOCAB_FILES_MAP
				lowercase__      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				lowercase__      =				['input_ids', 'attention_mask']



				def __init__( self    ,		__a    ,		__a="<s>"    ,		__a="</s>"    ,		__a="<unk>"    ,		__a="<pad>"    ,		__a = None    ,		**__a    ,		)	->	Tuple:






									'''simple docstring'''
									_UpperCamelCase										=  {} if sp_model_kwargs is None else sp_model_kwargs

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

									_UpperCamelCase										=  vocab_file

									_UpperCamelCase										=  spm.SentencePieceProcessor(**self.sp_model_kwargs)
									self.sp_model.Load(_lowerCAmelCase)



				@property
				def 						UpperCAmelCase							( self)	->	Optional[Any]:






									'''simple docstring'''
									return self.sp_model.get_piece_size()



				def 						UpperCAmelCase							( self)	->	int:






									'''simple docstring'''
									_UpperCamelCase										=  {self.convert_ids_to_tokens(_lowerCAmelCase): i for i in range(self.vocab_size)}
									vocab.update(self.added_tokens_encoder)
									return vocab



				def __getstate__( self)	->	List[Any]:






									'''simple docstring'''
									_UpperCamelCase										=  self.__dict__.copy()
									_UpperCamelCase										=  None
									return state



				def __setstate__( self    ,		__a)	->	Tuple:






									'''simple docstring'''
									_UpperCamelCase										=  d

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

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



				def 						UpperCAmelCase							( self    ,		__a)	->	Optional[int]:






									'''simple docstring'''
									return self.sp_model.encode(_lowerCAmelCase    ,		out_type=_lowerCAmelCase)



				def 						UpperCAmelCase							( self    ,		__a)	->	Optional[int]:






									'''simple docstring'''
									return self.sp_model.piece_to_id(_lowerCAmelCase)



				def 						UpperCAmelCase							( self    ,		__a)	->	int:






									'''simple docstring'''
									_UpperCamelCase										=  self.sp_model.IdToPiece(_lowerCAmelCase)
									return token



				def 						UpperCAmelCase							( self    ,		__a)	->	Optional[Any]:






									'''simple docstring'''
									_UpperCamelCase										=  []
									_UpperCamelCase										=  ''''''
									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(_lowerCAmelCase) + token
																			_UpperCamelCase										=  []
														else:
																			current_sub_tokens.append(_lowerCAmelCase)
									out_string += self.sp_model.decode(_lowerCAmelCase)
									return out_string.strip()



				def 						UpperCAmelCase							( self    ,		__a    ,		__a=None)	->	Union[str, Any]:






									'''simple docstring'''

									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 						UpperCAmelCase							( self    ,		__a    ,		__a = None    ,		__a = False)	->	Optional[Any]:






									'''simple docstring'''

									if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=_lowerCAmelCase    ,		token_ids_a=_lowerCAmelCase    ,		already_has_special_tokens=_lowerCAmelCase)

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




				def 						UpperCAmelCase							( self    ,		__a    ,		__a = None)	->	Tuple:






									'''simple docstring'''

									if not os.path.isdir(_lowerCAmelCase):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
														return
									_UpperCamelCase										=  os.path.join(
									    _lowerCAmelCase    ,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

									return (out_vocab_file,)








"""simple docstring"""





import copy
from collections import OrderedDict
from typing import Dict, Mapping

from packaging import version

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


_a														=		logging.get_logger(__name__)

_a														=		{
    """facebook/detr-resnet-50""": """https://huggingface.co/facebook/detr-resnet-50/resolve/main/config.json""",
    # See all DETR models at https://huggingface.co/models?filter=detr
}






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'detr'
		lowercase__      =				['past_key_values']
		lowercase__      =				{
		    'hidden_size': 'd_model',
		    'num_attention_heads': 'encoder_attention_heads',
		}



		def __init__( self    ,		__a=True    ,		__a=None    ,		__a=3    ,		__a=1_00    ,		__a=6    ,		__a=20_48    ,		__a=8    ,		__a=6    ,		__a=20_48    ,		__a=8    ,		__a=0.0    ,		__a=0.0    ,		__a=True    ,		__a="relu"    ,		__a=2_56    ,		__a=0.1    ,		__a=0.0    ,		__a=0.0    ,		__a=0.02    ,		__a=1.0    ,		__a=False    ,		__a="sine"    ,		__a="resnet50"    ,		__a=True    ,		__a=False    ,		__a=1    ,		__a=5    ,		__a=2    ,		__a=1    ,		__a=1    ,		__a=5    ,		__a=2    ,		__a=0.1    ,		**__a    ,		)	->	Tuple:






							'''simple docstring'''
							if backbone_config is not None and use_timm_backbone:
												raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''')

							if not use_timm_backbone:
												if backbone_config is None:
																	logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''')
																	_UpperCamelCase										=  CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''])
												elif isinstance(__a    ,		__a):
																	_UpperCamelCase										=  backbone_config.get('''model_type''')
																	_UpperCamelCase										=  CONFIG_MAPPING[backbone_model_type]
																	_UpperCamelCase										=  config_class.from_dict(__a)
												# set timm attributes to None
												_UpperCamelCase			,  _UpperCamelCase			,  _UpperCamelCase										=  None, None, None

							_UpperCamelCase										=  use_timm_backbone
							_UpperCamelCase										=  backbone_config
							_UpperCamelCase										=  num_channels
							_UpperCamelCase										=  num_queries
							_UpperCamelCase										=  d_model
							_UpperCamelCase										=  encoder_ffn_dim
							_UpperCamelCase										=  encoder_layers
							_UpperCamelCase										=  encoder_attention_heads
							_UpperCamelCase										=  decoder_ffn_dim
							_UpperCamelCase										=  decoder_layers
							_UpperCamelCase										=  decoder_attention_heads
							_UpperCamelCase										=  dropout
							_UpperCamelCase										=  attention_dropout
							_UpperCamelCase										=  activation_dropout
							_UpperCamelCase										=  activation_function
							_UpperCamelCase										=  init_std
							_UpperCamelCase										=  init_xavier_std
							_UpperCamelCase										=  encoder_layerdrop
							_UpperCamelCase										=  decoder_layerdrop
							_UpperCamelCase										=  encoder_layers
							_UpperCamelCase										=  auxiliary_loss
							_UpperCamelCase										=  position_embedding_type
							_UpperCamelCase										=  backbone
							_UpperCamelCase										=  use_pretrained_backbone
							_UpperCamelCase										=  dilation
							# Hungarian matcher
							_UpperCamelCase										=  class_cost
							_UpperCamelCase										=  bbox_cost
							_UpperCamelCase										=  giou_cost
							# Loss coefficients
							_UpperCamelCase										=  mask_loss_coefficient
							_UpperCamelCase										=  dice_loss_coefficient
							_UpperCamelCase										=  bbox_loss_coefficient
							_UpperCamelCase										=  giou_loss_coefficient
							_UpperCamelCase										=  eos_coefficient
							super().__init__(is_encoder_decoder=__a    ,		**__a)



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.encoder_attention_heads



		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return self.d_model



		@classmethod
		def 						UpperCAmelCase							( cls    ,		__a    ,		**__a)	->	Any:






							'''simple docstring'''
							return cls(backbone_config=__a    ,		**__a)




		def 						UpperCAmelCase							( self)	->	Dict[str, any]:






							'''simple docstring'''
							_UpperCamelCase										=  copy.deepcopy(self.__dict__)
							if output["backbone_config"] is not None:
												_UpperCamelCase										=  self.backbone_config.to_dict()
							_UpperCamelCase										=  self.__class__.model_type
							return output






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				version.parse('1.11'     )



		@property
		def 						UpperCAmelCase							( self)	->	Mapping[str, Mapping[int, str]]:






							'''simple docstring'''
							return OrderedDict(
							    [
							        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
							        ('''pixel_mask''', {0: '''batch'''}),
							    ])



		@property
		def 						UpperCAmelCase							( self)	->	float:






							'''simple docstring'''
							return 1e-5




		@property
		def 						UpperCAmelCase							( self)	->	int:






							'''simple docstring'''
							return 12






