






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
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


__lowerCamelCase :      str										=				logging.get_logger(__name__)

__lowerCamelCase :      int										=				{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

__lowerCamelCase :      Optional[int]										=				{
    """vocab_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/vocab.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/vocab.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/vocab.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/vocab.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/merges.txt""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/merges.txt""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/merges.txt""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/merges.txt""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """gpt2""": """https://huggingface.co/gpt2/resolve/main/tokenizer.json""",
        """gpt2-medium""": """https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json""",
        """gpt2-large""": """https://huggingface.co/gpt2-large/resolve/main/tokenizer.json""",
        """gpt2-xl""": """https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json""",
        """distilgpt2""": """https://huggingface.co/distilgpt2/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase :      List[str]										=				{
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		VOCAB_FILES_NAMES
					a_     =		PRETRAINED_VOCAB_FILES_MAP
					a_     =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					a_     =		["input_ids", "attention_mask"]
					a_     =		GPTaTokenizer



					def __init__( self					:  Optional[int]							,		__A					:  Any=None							,		__A					:  List[str]=None							,		__A					:  List[Any]=None							,		__A					:  str="<|endoftext|>"							,		__A					:  Tuple="<|endoftext|>"							,		__A					:  Any="<|endoftext|>"							,		__A					:  Tuple=False							,		**__A					:  Tuple							,		):
						super().__init__(
						    __A							,		__A							,		tokenizer_file=__A							,		unk_token=__A							,		bos_token=__A							,		eos_token=__A							,		add_prefix_space=__A							,		**__A							,		)

						snake_case__			:				Any       =  kwargs.pop("add_bos_token"							,		__A	)

						snake_case__			:				Optional[int]       =  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
						if pre_tok_state.get("add_prefix_space"							,		__A	) != add_prefix_space:
							snake_case__			:				Optional[Any]       =  getattr(__A							,		pre_tok_state.pop("type"	)	)
							snake_case__			:				str       =  add_prefix_space
							snake_case__			:				List[Any]       =  pre_tok_class(**__A	)

						snake_case__			:				List[Any]       =  add_prefix_space



					def 	_lowercase					( self					:  Optional[int]							,		*__A					:  int							,		**__A					:  Optional[Any]	):
						snake_case__			:				Tuple       =  kwargs.get("is_split_into_words"							,		__A	)
						assert self.add_prefix_space or not is_split_into_words, (
						    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

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



					def 	_lowercase					( self					:  List[Any]							,		*__A					:  List[Any]							,		**__A					:  Tuple	):
						snake_case__			:				int       =  kwargs.get("is_split_into_words"							,		__A	)

						assert self.add_prefix_space or not is_split_into_words, (
						    f'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
						    "to use it with pretokenized inputs."
						)

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



					def 	_lowercase					( self					:  List[Any]							,		__A					:  str							,		__A					:  Optional[str] = None	):
						snake_case__			:				Union[str, Any]       =  self._tokenizer.model.save(__A							,		name=__A	)
						return tuple(__A	)






					def 	_lowercase					( self					:  Tuple							,		__A					:  "Conversation"	):
						snake_case__			:				Tuple       =  []
						for is_user, text in conversation.iter_texts():
							input_ids.extend(self.encode(__A							,		add_special_tokens=__A	) + [self.eos_token_id]	)

						if len(__A	) > self.model_max_length:
							snake_case__			:				List[Any]       =  input_ids[-self.model_max_length :]
						return input_ids











import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 							SCREAMING_SNAKE_CASE__						(    UpperCamelCase_						):





					"""simple docstring"""






					a_     =		["image_processor", "tokenizer"]
					a_     =		"ViltImageProcessor"
					a_     =		("BertTokenizer", "BertTokenizerFast")



					def __init__( self					:  Optional[int]							,		__A					:  Optional[int]=None							,		__A					:  Optional[Any]=None							,		**__A					:  int	):
						snake_case__			:				Union[str, Any]       =  None
						if "feature_extractor" in kwargs:
							warnings.warn(
							    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
							    " instead."							,		__A							,		)
							snake_case__			:				Tuple       =  kwargs.pop("feature_extractor"	)

						snake_case__			:				Any       =  image_processor if image_processor is not None else feature_extractor
						if image_processor is None:
							raise ValueError("You need to specify an `image_processor`."	)
						if tokenizer is None:
							raise ValueError("You need to specify a `tokenizer`."	)

						super().__init__(__A							,		__A	)
						snake_case__			:				Tuple       =  self.image_processor



					def __call__( self					:  List[Any]							,		__A					:  int							,		__A					:  Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,		__A					:  bool = True							,		__A					:  Union[bool, str, PaddingStrategy] = False							,		__A					:  Union[bool, str, TruncationStrategy] = None							,		__A					:  Optional[int] = None							,		__A					:  int = 0							,		__A					:  Optional[int] = None							,		__A					:  Optional[bool] = None							,		__A					:  Optional[bool] = None							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = False							,		__A					:  bool = True							,		__A					:  Optional[Union[str, TensorType]] = None							,		**__A					:  List[Any]							,		):
						snake_case__			:				Optional[int]       =  self.tokenizer(
						    text=__A							,		add_special_tokens=__A							,		padding=__A							,		truncation=__A							,		max_length=__A							,		stride=__A							,		pad_to_multiple_of=__A							,		return_token_type_ids=__A							,		return_attention_mask=__A							,		return_overflowing_tokens=__A							,		return_special_tokens_mask=__A							,		return_offsets_mapping=__A							,		return_length=__A							,		verbose=__A							,		return_tensors=__A							,		**__A							,		)
						# add pixel_values + pixel_mask
						snake_case__			:				Optional[Any]       =  self.image_processor(__A							,		return_tensors=__A	)
						encoding.update(__A	)

						return encoding



					def 	_lowercase					( self					:  Optional[Any]							,		*__A					:  List[str]							,		**__A					:  Optional[int]	):
						return self.tokenizer.batch_decode(*__A							,		**__A	)



					def 	_lowercase					( self					:  Dict							,		*__A					:  str							,		**__A					:  str	):
						return self.tokenizer.decode(*__A							,		**__A	)



					@property
					def 	_lowercase					( self					:  str	):
						snake_case__			:				Optional[Any]       =  self.tokenizer.model_input_names
						snake_case__			:				Any       =  self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names	)	)



					@property
					def 	_lowercase					( self					:  List[Any]	):
						warnings.warn(
						    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,		__A							,		)
						return self.image_processor_class






					@property
					def 	_lowercase					( self					:  str	):
						warnings.warn(
						    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,		__A							,		)
						return self.image_processor



