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

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_xglm import XGLMTokenizer
else:
     __magic_name__   				=     None


__magic_name__   				=     logging.get_logger(__name__)

__magic_name__   				=     {'vocab_file': 'sentencepiece.bpe.model', 'tokenizer_file': 'tokenizer.json'}

__magic_name__   				=     {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    },
    'tokenizer_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/tokenizer.json',
    },
}

__magic_name__   				=     {
    'facebook/xglm-564M': 2_048,
}




class     _SCREAMING_SNAKE_CASE     (					_A       ):
      _A  :						List[Any]					=   VOCAB_FILES_NAMES
      _A  :						Union[str, Any]					=   PRETRAINED_VOCAB_FILES_MAP
      _A  :						Optional[int]					=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A  :						str					=   ['input_ids', 'attention_mask']
      _A  :						Tuple					=   XGLMTokenizer
      def __init__(	self						,		lowerCamelCase=None						,		lowerCamelCase=None						,		lowerCamelCase="<s>"						,		lowerCamelCase="</s>"						,		lowerCamelCase="</s>"						,		lowerCamelCase="<s>"						,		lowerCamelCase="<unk>"						,		lowerCamelCase="<pad>"						,		**lowerCamelCase						,		):
          # Compatibility with the original tokenizer
          snake_case__  = 7
          snake_case__  = [F"""<madeupword{i}>""" for i in range(self.num_madeup_words       )]

          snake_case__  = kwargs.get("additional_special_tokens"						,		[]       )
          kwargs["additional_special_tokens"] += [
              word for word in madeup_words if word not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              __SCREAMING_SNAKE_CASE						,		tokenizer_file=__SCREAMING_SNAKE_CASE						,		bos_token=__SCREAMING_SNAKE_CASE						,		eos_token=__SCREAMING_SNAKE_CASE						,		sep_token=__SCREAMING_SNAKE_CASE						,		cls_token=__SCREAMING_SNAKE_CASE						,		unk_token=__SCREAMING_SNAKE_CASE						,		pad_token=__SCREAMING_SNAKE_CASE						,		**__SCREAMING_SNAKE_CASE						,		)

          snake_case__  = vocab_file
          snake_case__  = False if not self.vocab_file else True
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase = None       ):

          if token_ids_a is None:
              return [self.sep_token_id] + token_ids_a
          snake_case__  = [self.sep_token_id]
          return sep + token_ids_a + sep + sep + token_ids_a
      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase = None       ):
          snake_case__  = [self.sep_token_id]

          if token_ids_a is None:
              return len(sep + token_ids_a       ) * [0]
          return len(sep + token_ids_a + sep + sep + token_ids_a       ) * [0]



      def A_	(	self						,		lowerCamelCase						,		lowerCamelCase = None       ):
          if not self.can_save_slow_tokenizer:
              raise ValueError(
                  "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
                  "tokenizer."       )

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

          if os.path.abspath(self.vocab_file       ) != os.path.abspath(__SCREAMING_SNAKE_CASE       ):
              copyfile(self.vocab_file						,		__SCREAMING_SNAKE_CASE       )

          return (out_vocab_file,)






"""simple docstring"""




import copy

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


__A     :    str    =		logging.get_logger(__name__)

__A     :    Union[str, Any]    =		{
    'facebook/sam-vit-huge': 'https://huggingface.co/facebook/sam-vit-huge/resolve/main/config.json',
    'facebook/sam-vit-large': 'https://huggingface.co/facebook/sam-vit-large/resolve/main/config.json',
    'facebook/sam-vit-base': 'https://huggingface.co/facebook/sam-vit-base/resolve/main/config.json',
}





class 			__UpperCamelCase (   _A						):


		def __init__(self :		int     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=2_5_6     ,     __SCREAMING_SNAKE_CASE :		List[str]=1_0_2_4     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1_6     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1_6     ,     __SCREAMING_SNAKE_CASE :		List[str]=4     ,     __SCREAMING_SNAKE_CASE :		List[Any]="gelu"     ,     __SCREAMING_SNAKE_CASE :		str=1E-6     ,     **__SCREAMING_SNAKE_CASE :		Optional[int]     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)
							A			=					hidden_size
							A			=					image_size
							A			=					patch_size
							A			=					image_size // patch_size
							A			=					mask_input_channels
							A			=					num_point_embeddings
							A			=					hidden_act
							A			=					layer_norm_eps





class 			__UpperCamelCase (   _A						):


		def __init__(self :		List[Any]     ,     __SCREAMING_SNAKE_CASE :		Tuple=2_5_6     ,     __SCREAMING_SNAKE_CASE :		str="relu"     ,     __SCREAMING_SNAKE_CASE :		List[Any]=2_0_4_8     ,     __SCREAMING_SNAKE_CASE :		Tuple=2     ,     __SCREAMING_SNAKE_CASE :		Any=8     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=2     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=3     ,     __SCREAMING_SNAKE_CASE :		List[Any]=3     ,     __SCREAMING_SNAKE_CASE :		Tuple=2_5_6     ,     __SCREAMING_SNAKE_CASE :		int=1E-6     ,     **__SCREAMING_SNAKE_CASE :		int     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)
							A			=					hidden_size
							A			=					hidden_act
							A			=					mlp_dim
							A			=					num_hidden_layers
							A			=					num_attention_heads
							A			=					attention_downsample_rate
							A			=					num_multimask_outputs
							A			=					iou_head_depth
							A			=					iou_head_hidden_dim
							A			=					layer_norm_eps





class 			__UpperCamelCase (   _A						):


		def __init__(self :		List[str]     ,     __SCREAMING_SNAKE_CASE :		List[Any]=7_6_8     ,     __SCREAMING_SNAKE_CASE :		int=2_5_6     ,     __SCREAMING_SNAKE_CASE :		Tuple=1_2     ,     __SCREAMING_SNAKE_CASE :		str=1_2     ,     __SCREAMING_SNAKE_CASE :		Any=3     ,     __SCREAMING_SNAKE_CASE :		Dict=1_0_2_4     ,     __SCREAMING_SNAKE_CASE :		str=1_6     ,     __SCREAMING_SNAKE_CASE :		Tuple="gelu"     ,     __SCREAMING_SNAKE_CASE :		int=1E-06     ,     __SCREAMING_SNAKE_CASE :		Dict=0.0     ,     __SCREAMING_SNAKE_CASE :		str=1E-10     ,     __SCREAMING_SNAKE_CASE :		List[str]=True     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=4.0     ,     __SCREAMING_SNAKE_CASE :		str=True     ,     __SCREAMING_SNAKE_CASE :		Any=True     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=1_4     ,     __SCREAMING_SNAKE_CASE :		Tuple=[2, 5, 8, 1_1]     ,     __SCREAMING_SNAKE_CASE :		str=1_2_8     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=None     ,     **__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)

							A			=					hidden_size
							A			=					output_channels
							A			=					num_hidden_layers
							A			=					num_attention_heads
							A			=					num_channels
							A			=					image_size
							A			=					patch_size
							A			=					hidden_act
							A			=					layer_norm_eps
							A			=					attention_dropout
							A			=					initializer_range
							A			=					qkv_bias
							A			=					mlp_ratio
							A			=					use_abs_pos
							A			=					use_rel_pos
							A			=					window_size
							A			=					global_attn_indexes
							A			=					num_pos_feats
							A			=					int(hidden_size * mlp_ratio) if mlp_dim is None else mlp_dim





class 			__UpperCamelCase (   _A						):
		SCREAMING_SNAKE_CASE								=    "sam"
		SCREAMING_SNAKE_CASE								=    True


		def __init__(self :		Optional[int]     ,     __SCREAMING_SNAKE_CASE :		Dict=None     ,     __SCREAMING_SNAKE_CASE :		Union[str, Any]=None     ,     __SCREAMING_SNAKE_CASE :		Optional[int]=None     ,     __SCREAMING_SNAKE_CASE :		List[Any]=0.0_2     ,     **__SCREAMING_SNAKE_CASE :		Optional[Any]     ,     ):
							super().__init__(**__SCREAMING_SNAKE_CASE)
							A			=					vision_config if vision_config is not None else {}
							A			=					prompt_encoder_config if prompt_encoder_config is not None else {}
							A			=					mask_decoder_config if mask_decoder_config is not None else {}

							if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												A			=					vision_config.to_dict()
							if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												A			=					prompt_encoder_config.to_dict()
							if isinstance(__SCREAMING_SNAKE_CASE     ,     __SCREAMING_SNAKE_CASE):
												A			=					mask_decoder_config.to_dict()

							A			=					SamVisionConfig(**__SCREAMING_SNAKE_CASE)
							A			=					SamPromptEncoderConfig(**__SCREAMING_SNAKE_CASE)
							A			=					SamMaskDecoderConfig(**__SCREAMING_SNAKE_CASE)
							A			=					initializer_range


		def 					SCREAMING_SNAKE_CASE__   (self :		List[str]):
							A			=					copy.deepcopy(self.__dict__)
							A			=					self.vision_config.to_dict()
							A			=					self.prompt_encoder_config.to_dict()
							A			=					self.mask_decoder_config.to_dict()
							A			=					self.__class__.model_type
							return output





