import copy
import random

from transformers import CLIPTokenizer




class     _snake_case  (  snake_case__ ):
					def __init__(     self: List[str]		,     *__lowerCamelCase: Optional[int]		,     **__lowerCamelCase: int   )			-> List[str]:
												super().__init__(*__lowerCamelCase		,     **__lowerCamelCase   )
												__UpperCAmelCase  :	List[Any]   			=      {}
					def       _lowerCamelCase       (     self: str		,     __lowerCamelCase: List[str]		,     *__lowerCamelCase: Dict		,     **__lowerCamelCase: List[str]   )			-> Optional[Any]:
												__UpperCAmelCase  :	Optional[Any]   			=      super().add_tokens(__lowerCamelCase		,     *__lowerCamelCase		,     **__lowerCamelCase   )
												if num_added_tokens == 0:
																			raise ValueError(
																			    f'''The tokenizer already contains the token {placeholder_token}. Please pass a different'''
																			    " `placeholder_token` that is not already in the tokenizer."   )
					def       _lowerCamelCase       (     self: Dict		,     __lowerCamelCase: int		,     *__lowerCamelCase: int		,     __lowerCamelCase: Dict=1		,     **__lowerCamelCase: Any   )			-> List[Any]:
												__UpperCAmelCase  :	Tuple   			=      []
												if num_vec_per_token == 1:
																			self.try_adding_tokens(__lowerCamelCase		,     *__lowerCamelCase		,     **__lowerCamelCase   )
																			output.append(__lowerCamelCase   )
												else:
																			__UpperCAmelCase  :	Tuple   			=      []
																			for i in range(__lowerCamelCase   ):
																										__UpperCAmelCase  :	Union[str, Any]   			=      placeholder_token + f'''_{i}'''
																										self.try_adding_tokens(__lowerCamelCase		,     *__lowerCamelCase		,     **__lowerCamelCase   )
																										output.append(__lowerCamelCase   )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
												for token in self.token_map:
																			if token in placeholder_token:
																										raise ValueError(
																										    f'''The tokenizer already has placeholder token {token} that can get confused with'''
																										    f''' {placeholder_token}keep placeholder tokens independent'''   )
												__UpperCAmelCase  :	List[Any]   			=      output
					def       _lowerCamelCase       (     self: Tuple		,     __lowerCamelCase: int		,     __lowerCamelCase: Optional[int]=False		,     __lowerCamelCase: Any=1.0   )			-> List[Any]:







												if isinstance(__lowerCamelCase		,     __lowerCamelCase   ):
																			__UpperCAmelCase  :	List[Any]   			=      []
																			for i in range(len(__lowerCamelCase   )   ):
																										output.append(self.replace_placeholder_tokens_in_text(text[i]		,     vector_shuffle=__lowerCamelCase   )   )
																			return output
												for placeholder_token in self.token_map:
																			if placeholder_token in text:
																										__UpperCAmelCase  :	str   			=      self.token_map[placeholder_token]
																										__UpperCAmelCase  :	Dict   			=      tokens[: 1 + int(len(__lowerCamelCase   ) * prop_tokens_to_load   )]
																										if vector_shuffle:
																																	__UpperCAmelCase  :	Any   			=      copy.copy(__lowerCamelCase   )
																																	random.shuffle(__lowerCamelCase   )
																										__UpperCAmelCase  :	int   			=      text.replace(__lowerCamelCase		,     " ".join(__lowerCamelCase   )   )
												return text
					def __call__(     self: List[str]		,     __lowerCamelCase: Union[str, Any]		,     *__lowerCamelCase: int		,     __lowerCamelCase: Optional[Any]=False		,     __lowerCamelCase: Any=1.0		,     **__lowerCamelCase: Union[str, Any]   )			-> Union[str, Any]:
												return super().__call__(
												    self.replace_placeholder_tokens_in_text(
												        __lowerCamelCase		,     vector_shuffle=__lowerCamelCase		,     prop_tokens_to_load=__lowerCamelCase   )		,     *__lowerCamelCase		,     **__lowerCamelCase		,     )





					def       _lowerCamelCase       (     self: Dict		,     __lowerCamelCase: Union[str, Any]		,     *__lowerCamelCase: int		,     __lowerCamelCase: Optional[int]=False		,     __lowerCamelCase: List[Any]=1.0		,     **__lowerCamelCase: Any   )			-> str:
												return super().encode(
												    self.replace_placeholder_tokens_in_text(
												        __lowerCamelCase		,     vector_shuffle=__lowerCamelCase		,     prop_tokens_to_load=__lowerCamelCase   )		,     *__lowerCamelCase		,     **__lowerCamelCase		,     )












from collections import OrderedDict
from typing import Mapping

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


_a      	=  logging.get_logger(__name__)

_a      	=  {
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	UpperCamelCase__								=      "distilbert"
	UpperCamelCase__								=      {
	    "hidden_size": "dim",
	    "num_attention_heads": "n_heads",
	    "num_hidden_layers": "n_layers",
	}

	def __init__(							self       ,    UpperCAmelCase=3_0522       ,    UpperCAmelCase=512       ,    UpperCAmelCase=False       ,    UpperCAmelCase=6       ,    UpperCAmelCase=12       ,    UpperCAmelCase=768       ,    UpperCAmelCase=4 * 768       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase="gelu"       ,    UpperCAmelCase=0.02       ,    UpperCAmelCase=0.1       ,    UpperCAmelCase=0.2       ,    UpperCAmelCase=0       ,    **UpperCAmelCase       ,    ):
							"""simple docstring"""







							_UpperCAmelCase									=			vocab_size
							_UpperCAmelCase									=			max_position_embeddings
							_UpperCAmelCase									=			sinusoidal_pos_embds
							_UpperCAmelCase									=			n_layers
							_UpperCAmelCase									=			n_heads
							_UpperCAmelCase									=			dim
							_UpperCAmelCase									=			hidden_dim
							_UpperCAmelCase									=			dropout
							_UpperCAmelCase									=			attention_dropout
							_UpperCAmelCase									=			activation
							_UpperCAmelCase									=			initializer_range
							_UpperCAmelCase									=			qa_dropout
							_UpperCAmelCase									=			seq_classif_dropout
							super().__init__(**UpperCAmelCase       ,    pad_token_id=UpperCAmelCase    )





class __lowerCamelCase (      snake_case__):





	"""simple docstring"""






	@property
	def        UpperCamelCase    (							self    ):
							"""simple docstring"""







							if self.task == "multiple-choice":
													_UpperCAmelCase									=			{0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
													_UpperCAmelCase									=			{0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ]    )
