





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {'vocab_file': 'spiece.model'}

UpperCAmelCase_     							= {
    'vocab_file': {
        'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model',
        'google/bigbird-roberta-large': (
            'https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'
        ),
        'google/bigbird-base-trivia-itc': (
            'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'
        ),
    }
}

UpperCAmelCase_     							= {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Optional[Any]   		=		VOCAB_FILES_NAMES
   a						:							Optional[Any]   		=		PRETRAINED_VOCAB_FILES_MAP
   a						:							Optional[int]   		=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a						:							int   		=		["input_ids", "attention_mask"]
   a						:							List[int]   		=		[]




   def __init__(    self,		__magic_name__,		__magic_name__="<unk>",		__magic_name__="<s>",		__magic_name__="</s>",		__magic_name__="<pad>",		__magic_name__="[SEP]",		__magic_name__="[MASK]",		__magic_name__="[CLS]",		__magic_name__ = None,		**__magic_name__,		) ->      None:






       """simple docstring"""
       UpperCamelCase__							:				int          =      AddedToken(__magic_name__,		lstrip=__magic_name__,		rstrip=__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else bos_token
       UpperCamelCase__							:				Tuple          =      AddedToken(__magic_name__,		lstrip=__magic_name__,		rstrip=__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else eos_token
       UpperCamelCase__							:				Tuple          =      AddedToken(__magic_name__,		lstrip=__magic_name__,		rstrip=__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else unk_token
       UpperCamelCase__							:				str          =      AddedToken(__magic_name__,		lstrip=__magic_name__,		rstrip=__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else pad_token
       UpperCamelCase__							:				List[Any]          =      AddedToken(__magic_name__,		lstrip=__magic_name__,		rstrip=__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else cls_token
       UpperCamelCase__							:				Tuple          =      AddedToken(__magic_name__,		lstrip=__magic_name__,		rstrip=__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else sep_token

       # Mask token behave like a normal word, i.e. include the space before it
       UpperCamelCase__							:				List[Any]          =      AddedToken(__magic_name__,		lstrip=__magic_name__,		rstrip=__magic_name__							) if isinstance(__magic_name__,		__magic_name__							) else mask_token

       UpperCamelCase__							:				int          =      {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           bos_token=__magic_name__,		eos_token=__magic_name__,		unk_token=__magic_name__,		pad_token=__magic_name__,		sep_token=__magic_name__,		mask_token=__magic_name__,		cls_token=__magic_name__,		sp_model_kwargs=self.sp_model_kwargs,		**__magic_name__,		)

       UpperCamelCase__							:				List[Any]          =      vocab_file

       UpperCamelCase__							:				Dict          =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(__magic_name__							)




   @property
   def       UpperCamelCase__  (    self							) ->      str:






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




   def       UpperCamelCase__  (    self							) ->      Dict:






       """simple docstring"""
       UpperCamelCase__							:				Union[str, Any]          =      {self.convert_ids_to_tokens(__magic_name__							): i for i in range(self.vocab_size							)}
       vocab.update(self.added_tokens_encoder							)
       return vocab




   def __getstate__(    self							) ->      Union[str, Any]:






       """simple docstring"""
       UpperCamelCase__							:				str          =      self.__dict__.copy()
       UpperCamelCase__							:				Dict          =      None
       return state




   def __setstate__(    self,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				str          =      d

       # for backward compatibility
       if not hasattr(self,		'''sp_model_kwargs'''							):
           UpperCamelCase__							:				Optional[int]          =      {}

       UpperCamelCase__							:				Optional[Any]          =      spm.SentencePieceProcessor(**self.sp_model_kwargs							)
       self.sp_model.Load(self.vocab_file							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       return self.sp_model.encode(__magic_name__,		out_type=__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      Optional[int]:






       """simple docstring"""
       return self.sp_model.piece_to_id(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      self.sp_model.IdToPiece(__magic_name__							)
       return token




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      List[str]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      []
       UpperCamelCase__							:				Any          =      ''''''
       UpperCamelCase__							:				int          =      False
       for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
               if not prev_is_special:
                   out_string += " "
               out_string += self.sp_model.decode(__magic_name__							) + token
               UpperCamelCase__							:				Tuple          =      True
               UpperCamelCase__							:				Union[str, Any]          =      []
           else:
               current_sub_tokens.append(__magic_name__							)
               UpperCamelCase__							:				List[str]          =      False
       out_string += self.sp_model.decode(__magic_name__							)
       return out_string.strip()




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = False,		__magic_name__ = None,		__magic_name__ = True,		**__magic_name__,		) ->      str:






       """simple docstring"""
       UpperCamelCase__							:				Any          =      kwargs.pop('''use_source_tokenizer''',		__magic_name__							)

       UpperCamelCase__							:				str          =      self.convert_ids_to_tokens(__magic_name__,		skip_special_tokens=__magic_name__							)

       # To avoid mixing byte-level and unicode for byte-level BPT
       # we need to build string separately for added tokens and byte-level tokens
       # cf. https://github.com/huggingface/transformers/issues/1133
       UpperCamelCase__							:				Optional[int]          =      []
       UpperCamelCase__							:				List[str]          =      []
       for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
               continue
           if token in self.added_tokens_encoder:
               if current_sub_text:
                   sub_texts.append(self.convert_tokens_to_string(__magic_name__							)							)
                   UpperCamelCase__							:				Dict          =      []
               sub_texts.append(__magic_name__							)
           else:
               current_sub_text.append(__magic_name__							)
       if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(__magic_name__							)							)

       # Mimic the behavior of the Rust tokenizer:
       # No space before [MASK] and [SEP]
       if spaces_between_special_tokens:
           UpperCamelCase__							:				Optional[Any]          =      re.sub(R''' (\[(MASK|SEP)\])''',		R'''\1''',		''' '''.join(__magic_name__							)							)
       else:
           UpperCamelCase__							:				Tuple          =      ''''''.join(__magic_name__							)

       UpperCamelCase__							:				Dict          =      (
           clean_up_tokenization_spaces
           if clean_up_tokenization_spaces is not None
           else self.clean_up_tokenization_spaces
       )
       if clean_up_tokenization_spaces:
           UpperCamelCase__							:				str          =      self.clean_up_tokenization(__magic_name__							)
           return clean_text
       else:
           return text




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None							) ->      Tuple[str]:






       """simple docstring"""
       if not os.path.isdir(__magic_name__							):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"							)
           return
       UpperCamelCase__							:				Dict          =      os.path.join(
           __magic_name__,		(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)

       if os.path.abspath(self.vocab_file							) != os.path.abspath(__magic_name__							) and os.path.isfile(self.vocab_file							):
           copyfile(self.vocab_file,		__magic_name__							)
       elif not os.path.isfile(self.vocab_file							):
           with open(__magic_name__,		'''wb'''							) as fi:
               UpperCamelCase__							:				Union[str, Any]          =      self.sp_model.serialized_model_proto()
               fi.write(__magic_name__							)

       return (out_vocab_file,)




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None							) ->      List[int]:






       """simple docstring"""
       if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       UpperCamelCase__							:				Dict          =      [self.cls_token_id]
       UpperCamelCase__							:				Tuple          =      [self.sep_token_id]
       return cls + token_ids_a + sep + token_ids_a + sep




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None,		__magic_name__ = False							) ->      List[int]:






       """simple docstring"""
       if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=__magic_name__,		token_ids_a=__magic_name__,		already_has_special_tokens=__magic_name__							)

       if token_ids_a is None:
           return [1] + ([0] * len(__magic_name__							)) + [1]
       return [1] + ([0] * len(__magic_name__							)) + [1] + ([0] * len(__magic_name__							)) + [1]




   def       UpperCamelCase__  (    self,		__magic_name__,		__magic_name__ = None							) ->      List[int]:






       """simple docstring"""
       UpperCamelCase__							:				Optional[int]          =      [self.sep_token_id]
       UpperCamelCase__							:				Any          =      [self.cls_token_id]
       if token_ids_a is None:
           return len(cls + token_ids_a + sep							) * [0]
       return len(cls + token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]








from collections import OrderedDict
from typing import Mapping

from packaging import version

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


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    'google/mobilenet_v2_1.4_224': 'https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json',
    'google/mobilenet_v2_1.0_224': 'https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json',
    'google/mobilenet_v2_0.75_160': 'https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json',
    'google/mobilenet_v2_0.35_96': 'https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		"mobilenet_v2"




   def __init__(    self,		__magic_name__=3,		__magic_name__=224,		__magic_name__=1.0,		__magic_name__=8,		__magic_name__=8,		__magic_name__=6,		__magic_name__=32,		__magic_name__=True,		__magic_name__=True,		__magic_name__="relu6",		__magic_name__=True,		__magic_name__=0.8,		__magic_name__=0.02,		__magic_name__=0.001,		__magic_name__=255,		**__magic_name__,		) ->      List[Any]:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       if depth_multiplier <= 0:
           raise ValueError('''depth_multiplier must be greater than zero.'''							)

       UpperCamelCase__							:				Union[str, Any]          =      num_channels
       UpperCamelCase__							:				int          =      image_size
       UpperCamelCase__							:				int          =      depth_multiplier
       UpperCamelCase__							:				Tuple          =      depth_divisible_by
       UpperCamelCase__							:				List[str]          =      min_depth
       UpperCamelCase__							:				Optional[int]          =      expand_ratio
       UpperCamelCase__							:				Optional[int]          =      output_stride
       UpperCamelCase__							:				Tuple          =      first_layer_is_expansion
       UpperCamelCase__							:				Union[str, Any]          =      finegrained_output
       UpperCamelCase__							:				str          =      hidden_act
       UpperCamelCase__							:				Optional[Any]          =      tf_padding
       UpperCamelCase__							:				Optional[int]          =      classifier_dropout_prob
       UpperCamelCase__							:				int          =      initializer_range
       UpperCamelCase__							:				Union[str, Any]          =      layer_norm_eps
       UpperCamelCase__							:				Tuple          =      semantic_loss_ignore_index






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Union[str, Any]   		=		version.parse("1.11"				)




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






       """simple docstring"""
       return OrderedDict([('''pixel_values''', {0: '''batch'''})]							)




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






       """simple docstring"""
       if self.task == "image-classification":
           return OrderedDict([('''logits''', {0: '''batch'''})]							)
       else:
           return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})]							)




   @property
   def       UpperCamelCase__  (    self							) ->      float:






       """simple docstring"""
       return 1E-4

