



"""simple docstring"""


import copy
from typing import Dict

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
from ..bit import BitConfig


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{
    """google/vit-hybrid-base-bit-384""": """https://huggingface.co/vit-hybrid-base-bit-384/resolve/main/config.json""",
    # See all ViT hybrid models at https://huggingface.co/models?filter=vit
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				"""vit-hybrid"""






						def __init__(   self					:Union[str, Any]  ,    lowercase_					:Dict=None  ,    lowercase_					:Union[str, Any]=7_68  ,    lowercase_					:Optional[int]=12  ,    lowercase_					:Tuple=12  ,    lowercase_					:Tuple=30_72  ,    lowercase_					:List[Any]="gelu"  ,    lowercase_					:Any=0.0  ,    lowercase_					:Tuple=0.0  ,    lowercase_					:Optional[Any]=0.02  ,    lowercase_					:List[str]=1E-12  ,    lowercase_					:Optional[int]=2_24  ,    lowercase_					:List[str]=1  ,    lowercase_					:Union[str, Any]=3  ,    lowercase_					:int=[1, 10_24, 24, 24]  ,    lowercase_					:Optional[Any]=True  ,    **lowercase_					:List[str]  ,    )							->     List[str]:
												super().__init__(**lowercase_   )

												if backbone_config is None:
																		logger.info('`backbone_config` is `None`. Initializing the config with a `BiT` backbone.'   )
																		UpperCAmelCase			 =					{
																		    'global_padding': 'same',
																		    'layer_type': 'bottleneck',
																		    'depths': [3, 4, 9],
																		    'out_features': ['stage3'],
																		    'embedding_dynamic_padding': True,
																		}

												if isinstance(lowercase_  ,    lowercase_   ):
																		if "model_type" in backbone_config:
																								UpperCAmelCase			 =					CONFIG_MAPPING[backbone_config['model_type']]
																		else:
																								logger.info(
																								    '`model_type` is not found in `backbone_config`. Use `Bit` as the backbone configuration class.'   )
																								UpperCAmelCase			 =					BitConfig
																		UpperCAmelCase			 =					backbone_config_class(**lowercase_   )

												UpperCAmelCase			 =					backbone_featmap_shape
												UpperCAmelCase			 =					backbone_config
												UpperCAmelCase			 =					hidden_size
												UpperCAmelCase			 =					num_hidden_layers
												UpperCAmelCase			 =					num_attention_heads
												UpperCAmelCase			 =					intermediate_size
												UpperCAmelCase			 =					hidden_act
												UpperCAmelCase			 =					hidden_dropout_prob
												UpperCAmelCase			 =					attention_probs_dropout_prob
												UpperCAmelCase			 =					initializer_range
												UpperCAmelCase			 =					layer_norm_eps
												UpperCAmelCase			 =					image_size
												UpperCAmelCase			 =					patch_size
												UpperCAmelCase			 =					num_channels
												UpperCAmelCase			 =					qkv_bias






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











'''simple docstring'''





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

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


if is_sentencepiece_available():
  from .tokenization_reformer import ReformerTokenizer
else:
  a		:	Optional[int]       =      None


a		:	Optional[Any]       =      logging.get_logger(__name__)


a		:	Union[str, Any]       =      '''▁'''

a		:	Any       =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:	List[Any]       =      {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

a		:	Any       =      {
    '''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}




class      UpperCamelCase       (	snake_case__       ):
  a     :str          =					VOCAB_FILES_NAMES
  a     :List[Any]          =					PRETRAINED_VOCAB_FILES_MAP
  a     :Optional[int]          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     :Any          =					['input_ids', 'attention_mask']
  a     :int          =					ReformerTokenizer



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase="</s>"		,     __lowerCAmelCase="<unk>"		,     __lowerCAmelCase=[]		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      super().__init__(
          __lowerCAmelCase		,     tokenizer_file=__lowerCAmelCase		,     eos_token=__lowerCAmelCase		,     unk_token=__lowerCAmelCase		,     additional_special_tokens=__lowerCAmelCase		,     **__lowerCAmelCase		,     )

      _a				:							List[str]							       =      vocab_file
      _a				:							Optional[int]							       =      False if not self.vocab_file else True



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      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(__lowerCAmelCase ):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
          return
      _a				:							List[str]							       =      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 ):
          copyfile(self.vocab_file		,     __lowerCAmelCase )

      return (out_vocab_file,)


