



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


SCREAMING_SNAKE_CASE     				=						logging.get_logger(__name__)

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

SCREAMING_SNAKE_CASE     				=						{
    "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",
    },
}

SCREAMING_SNAKE_CASE     				=						{
    "gpt2": 1024,
    "gpt2-medium": 1024,
    "gpt2-large": 1024,
    "gpt2-xl": 1024,
    "distilgpt2": 1024,
}



class 	lowerCamelCase      (						lowercase__  ):







					'''simple docstring'''







					lowerCAmelCase_				:    List[str]									=  VOCAB_FILES_NAMES
					lowerCAmelCase_				:    str									=  PRETRAINED_VOCAB_FILES_MAP
					lowerCAmelCase_				:    int									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowerCAmelCase_				:    List[Any]									=  ['input_ids', 'attention_mask']
					lowerCAmelCase_				:    List[Any]									=  GPTaTokenizer



					def __init__(			self    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None    ,	lowerCAmelCase=None    ,	lowerCAmelCase="<|endoftext|>"    ,	lowerCAmelCase="<|endoftext|>"    ,	lowerCAmelCase="<|endoftext|>"    ,	lowerCAmelCase=False    ,	**lowerCAmelCase    ,	):
							super().__init__(
							    lowerCAmelCase    ,	lowerCAmelCase    ,	tokenizer_file=lowerCAmelCase    ,	unk_token=lowerCAmelCase    ,	bos_token=lowerCAmelCase    ,	eos_token=lowerCAmelCase    ,	add_prefix_space=lowerCAmelCase    ,	**lowerCAmelCase    ,	)

							UpperCAmelCase_						=				kwargs.pop("add_bos_token"    ,	lowerCAmelCase     )

							UpperCAmelCase_						=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
							if pre_tok_state.get("add_prefix_space"    ,	lowerCAmelCase     ) != add_prefix_space:
									UpperCAmelCase_						=				getattr(lowerCAmelCase    ,	pre_tok_state.pop("type"     )     )
									UpperCAmelCase_						=				add_prefix_space
									UpperCAmelCase_						=				pre_tok_class(**lowerCAmelCase     )

							UpperCAmelCase_						=				add_prefix_space



					def    A__		(			self    ,	*lowerCAmelCase    ,	**lowerCAmelCase     ):
							UpperCAmelCase_						=				kwargs.get("is_split_into_words"    ,	lowerCAmelCase     )
							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(*lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	*lowerCAmelCase    ,	**lowerCAmelCase     ):
							UpperCAmelCase_						=				kwargs.get("is_split_into_words"    ,	lowerCAmelCase     )

							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(*lowerCAmelCase    ,	**lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase    ,	lowerCAmelCase = None     ):
							UpperCAmelCase_						=				self._tokenizer.model.save(lowerCAmelCase    ,	name=lowerCAmelCase     )
							return tuple(lowerCAmelCase     )



					def    A__		(			self    ,	lowerCAmelCase     ):
							UpperCAmelCase_						=				[]
							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:
									UpperCAmelCase_						=				input_ids[-self.model_max_length :]
							return input_ids






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available


SCREAMING_SNAKE_CASE     				=						{"tokenization_herbert": ["HerbertTokenizer"]}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			SCREAMING_SNAKE_CASE     				=						["HerbertTokenizerFast"]


if TYPE_CHECKING:
			from .tokenization_herbert import HerbertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_herbert_fast import HerbertTokenizerFast

else:
			import sys

			SCREAMING_SNAKE_CASE     				=						_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

