


"""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
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": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}







class 							__magic_name__     (		__UpperCAmelCase   ):
					_SCREAMING_SNAKE_CASE       :					Optional[int]     							=   VOCAB_FILES_NAMES
					_SCREAMING_SNAKE_CASE       :					Any     							=   PRETRAINED_VOCAB_FILES_MAP
					_SCREAMING_SNAKE_CASE       :					Tuple     							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					_SCREAMING_SNAKE_CASE       :					Optional[Any]     							=   ['input_ids', 'attention_mask']
					_SCREAMING_SNAKE_CASE       :					int     							=   GPTaTokenizer





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

												__snake_case     			=       kwargs.pop("add_bos_token"							,						lowerCAmelCase_    )

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

												__snake_case     			=       add_prefix_space





					def 							lowerCAmelCase      (				self   :							int							,						*snake_case_   :							Optional[Any]							,						**snake_case_   :							Optional[Any]    ):
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Dict							,						*snake_case_   :							str							,						**snake_case_   :							Optional[int]    ):
												__snake_case     			=       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 							lowerCAmelCase      (				self   :							Dict							,						snake_case_   :							str							,						snake_case_   :							Optional[str] = None    ):
												__snake_case     			=       self._tokenizer.model.save(lowerCAmelCase_							,						name=lowerCAmelCase_    )
												return tuple(lowerCAmelCase_    )





					def 							lowerCAmelCase      (				self   :							List[str]							,						snake_case_   :							"Conversation"    ):
												__snake_case     			=       []
												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:
																			__snake_case     			=       input_ids[-self.model_max_length :]
												return input_ids


"""simple docstring"""







from __future__ import annotations

from dataclasses import dataclass







@dataclass
class 							__magic_name__     :
					_SCREAMING_SNAKE_CASE       :					float
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None
					_SCREAMING_SNAKE_CASE       :					TreeNode | None     							=   None




def   __UpperCamelCase   (					SCREAMING_SNAKE_CASE )  ->     bool:







							"""simple docstring"""


							def is_valid_tree(SCREAMING_SNAKE_CASE ) -> bool:
														if node is None:
																					return True

														if not isinstance(SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ):
																					return False

														try:
																					float(node.data )
														except (TypeError, ValueError):
																					return False

														return is_valid_tree(node.left ) and is_valid_tree(node.right )

							if not is_valid_tree(SCREAMING_SNAKE_CASE ):
														raise ValueError(
														    "Each node should be type of TreeNode and data should be float." )

							def is_binary_search_tree_recursive_check(
							    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE							,    SCREAMING_SNAKE_CASE ) -> bool:

														if node is None:
																					return True

														return (
														    left_bound < node.data < right_bound
														    and is_binary_search_tree_recursive_check(node.left							,    SCREAMING_SNAKE_CASE							,    node.data )
														    and is_binary_search_tree_recursive_check(
														        node.right							,    node.data							,    SCREAMING_SNAKE_CASE )
														)

							return is_binary_search_tree_recursive_check(SCREAMING_SNAKE_CASE							,    -float("inf" )							,    float("inf" ) )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
