



"""simple docstring"""






import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation


_lowercase : Union[str, Any]      						=						logging.get_logger(__name__)

_lowercase : Tuple      						=						{"tokenizer_file": "tokenizer.json"}

_lowercase : Dict      						=						{
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					_a   			=							VOCAB_FILES_NAMES
					_a   			=							PRETRAINED_VOCAB_FILES_MAP
					_a   			=							['input_ids', 'attention_mask']
					_a   			=							None



					def __init__(       self							:  Tuple,  lowerCamelCase							:  Tuple=None,  lowerCamelCase							:  int=None,  lowerCamelCase							:  List[Any]=None,  lowerCamelCase							:  Optional[Any]="<unk>",  lowerCamelCase							:  Optional[Any]="<s>",  lowerCamelCase							:  Tuple="</s>",  lowerCamelCase							:  Optional[int]="<pad>",  lowerCamelCase							:  Any=False,  lowerCamelCase							:  Optional[Any]=False,  **lowerCamelCase							:  Tuple,  )->					int:
								super().__init__(
								    lowerCamelCase,  lowerCamelCase,  tokenizer_file=lowerCamelCase,  unk_token=lowerCamelCase,  bos_token=lowerCamelCase,  eos_token=lowerCamelCase,  pad_token=lowerCamelCase,  add_prefix_space=lowerCamelCase,  clean_up_tokenization_spaces=lowerCamelCase,  **lowerCamelCase,  )
								lowerCamelCase__		:		Optional[int]			=json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
								if pre_tok_state.get('''add_prefix_space''',  lowerCamelCase     ) != add_prefix_space:
											lowerCamelCase__		:		Dict			=getattr(lowerCamelCase,  pre_tok_state.pop('''type'''     )     )
											lowerCamelCase__		:		List[str]			=add_prefix_space
											lowerCamelCase__		:		Optional[Any]			=pre_tok_class(**lowerCamelCase     )

								lowerCamelCase__		:		int			=add_prefix_space



					def 		snake_case       (       self							:  List[str],  *lowerCamelCase							:  Optional[int],  **lowerCamelCase							:  Union[str, Any]     )->					BatchEncoding:
								lowerCamelCase__		:		Dict			=kwargs.get('''is_split_into_words''',  lowerCamelCase     )
								if not (self.add_prefix_space or not is_split_into_words):
											raise Exception(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
											    ''' pretokenized inputs.'''     )

								return super()._batch_encode_plus(*lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Optional[Any],  *lowerCamelCase							:  List[str],  **lowerCamelCase							:  List[str]     )->					BatchEncoding:
								lowerCamelCase__		:		Optional[Any]			=kwargs.get('''is_split_into_words''',  lowerCamelCase     )

								if not (self.add_prefix_space or not is_split_into_words):
											raise Exception(
											    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
											    ''' pretokenized inputs.'''     )

								return super()._encode_plus(*lowerCamelCase,  **lowerCamelCase     )



					def 		snake_case       (       self							:  Dict,  lowerCamelCase							:  str,  lowerCamelCase							:  Optional[str] = None     )->					Tuple[str]:
								lowerCamelCase__		:		Optional[int]			=self._tokenizer.model.save(lowerCamelCase,  name=lowerCamelCase     )
								return tuple(lowerCamelCase     )




					def 		snake_case       (       self							:  Optional[Any],  lowerCamelCase							:  "Conversation"     )->					List[int]:
								lowerCamelCase__		:		Dict			=[]
								for is_user, text in conversation.iter_texts():
											input_ids.extend(self.encode(lowerCamelCase,  add_special_tokens=lowerCamelCase     ) + [self.eos_token_id]     )

								if len(lowerCamelCase     ) > self.model_max_length:
											lowerCamelCase__		:		Any			=input_ids[-self.model_max_length :]
								return input_ids









"""simple docstring"""






import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_lowercase : Any      						=						logging.get_logger(__name__)

class        __SCREAMING_SNAKE_CASE	(      lowerCAmelCase_   ):


					'''simple docstring'''





					def __init__(       self							:  Any,  *lowerCamelCase							:  str,  **lowerCamelCase							:  Optional[Any]     )->					None:
								warnings.warn(
								    '''The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
								    ''' use OwlViTImageProcessor instead.''',  lowerCamelCase,  )
								super().__init__(*lowerCamelCase,  **lowerCamelCase     )




