



from __future__ import annotations

from dataclasses import dataclass
@dataclass
class 				_snake_case			:
				UpperCamelCase__				:					float
				UpperCamelCase__				:					TreeNode | None     		=None
				UpperCamelCase__				:					TreeNode | None     		=None



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_  ):

			# Validation
			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()









import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowercase_								=     logging.get_logger(__name__)


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

lowercase_								=     {
    """vocab_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
    },
}

lowercase_								=     {
    """allenai/led-base-16384""": 1_6384,
}
class 				_snake_case			(					lowercase__):
				UpperCamelCase__				:					int     		=VOCAB_FILES_NAMES
				UpperCamelCase__				:					Any     		=PRETRAINED_VOCAB_FILES_MAP
				UpperCamelCase__				:					Dict     		=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				UpperCamelCase__				:					List[Any]     		=LEDTokenizer
				UpperCamelCase__				:					Tuple     		=["""input_ids""", """attention_mask"""]




				def __init__(						self					:						Optional[Any],					__lowercase					:						Optional[Any]=None,					__lowercase					:						Dict=None,					__lowercase					:						Tuple=None,					__lowercase					:						Union[str, Any]="replace",					__lowercase					:						Tuple="<s>",					__lowercase					:						Optional[Any]="</s>",					__lowercase					:						Tuple="</s>",					__lowercase					:						List[str]="<s>",					__lowercase					:						Tuple="<unk>",					__lowercase					:						Dict="<pad>",					__lowercase					:						Dict="<mask>",					__lowercase					:						Any=False,					__lowercase					:						Any=True,					**__lowercase					:						List[Any],					):
							super().__init__(
							    __lowercase,					__lowercase,					tokenizer_file=__lowercase,					errors=__lowercase,					bos_token=__lowercase,					eos_token=__lowercase,					sep_token=__lowercase,					cls_token=__lowercase,					unk_token=__lowercase,					pad_token=__lowercase,					mask_token=__lowercase,					add_prefix_space=__lowercase,					trim_offsets=__lowercase,					**__lowercase,					)

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

							lowercase__					  =				add_prefix_space

							# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
							lowercase__					  =				"post_processor"
							lowercase__					  =				getattr(self.backend_tokenizer,					__lowercase,					__lowercase						)
							if tokenizer_component_instance:
										lowercase__					  =				json.loads(tokenizer_component_instance.__getstate__()						)

										# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
										if "sep" in state:
													lowercase__					  =				tuple(state["sep"]						)
										if "cls" in state:
													lowercase__					  =				tuple(state["cls"]						)

										lowercase__					  =				False

										if state.get("add_prefix_space",					__lowercase						) != add_prefix_space:
													lowercase__					  =				add_prefix_space
													lowercase__					  =				True

										if state.get("trim_offsets",					__lowercase						) != trim_offsets:
													lowercase__					  =				trim_offsets
													lowercase__					  =				True

										if changes_to_apply:
													lowercase__					  =				getattr(__lowercase,					state.pop("type"						)						)
													lowercase__					  =				component_class(**__lowercase						)
													setattr(self.backend_tokenizer,					__lowercase,					__lowercase						)




				@property
				# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
				def 							A__      (						self					:						str						):
							if self._mask_token is None:
										if self.verbose:
													logger.error("Using mask_token, but it is not set yet."						)
										return None
							return str(self._mask_token						)




				@mask_token.setter
				def 							A__      (						self					:						Optional[int],					__lowercase					:						Dict						):
							lowercase__					  =				AddedToken(__lowercase,					lstrip=__lowercase,					rstrip=__lowercase						) if isinstance(__lowercase,					__lowercase						) else value
							lowercase__					  =				value




				def 							A__      (						self					:						Any,					*__lowercase					:						List[Any],					**__lowercase					:						Optional[Any]						):
							lowercase__					  =				kwargs.get("is_split_into_words",					__lowercase						)

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

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




				def 							A__      (						self					:						int,					*__lowercase					:						Union[str, Any],					**__lowercase					:						List[str]						):
							lowercase__					  =				kwargs.get("is_split_into_words",					__lowercase						)

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

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




				def 							A__      (						self					:						Optional[Any],					__lowercase					:						str,					__lowercase					:						Optional[str] = None						):
							lowercase__					  =				self._tokenizer.model.save(__lowercase,					name=__lowercase						)
							return tuple(__lowercase						)




				def 							A__      (						self					:						List[str],					__lowercase					:						int,					__lowercase					:						Optional[int]=None						):
							lowercase__					  =				[self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
										return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




				def 							A__      (						self					:						int,					__lowercase					:						List[int],					__lowercase					:						Optional[List[int]] = None						):
							lowercase__					  =				[self.sep_token_id]
							lowercase__					  =				[self.cls_token_id]

							if token_ids_a is None:
										return len(cls + token_ids_a + sep						) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]




				def 							A__      (						self					:						Union[str, Any],					__lowercase					:						Union[Dict[str, EncodedInput], BatchEncoding],					__lowercase					:						Optional[int] = None,					__lowercase					:						PaddingStrategy = PaddingStrategy.DO_NOT_PAD,					__lowercase					:						Optional[int] = None,					__lowercase					:						Optional[bool] = None,					):
							lowercase__					  =				super()._pad(
							    encoded_inputs=__lowercase,					max_length=__lowercase,					padding_strategy=__lowercase,					pad_to_multiple_of=__lowercase,					return_attention_mask=__lowercase,					)

							# Load from model defaults
							if return_attention_mask is None:
										lowercase__					  =				"attention_mask" in self.model_input_names

							if return_attention_mask and "global_attention_mask" in encoded_inputs:
										lowercase__					  =				encoded_inputs[self.model_input_names[0]]
										# `global_attention_mask` need to have the same length as other (sequential) inputs.
										lowercase__					  =				len(encoded_inputs["global_attention_mask"]						) != len(__lowercase						)

										if needs_to_be_padded:
													lowercase__					  =				len(__lowercase						) - len(encoded_inputs["global_attention_mask"]						)

													if self.padding_side == "right":
																# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																lowercase__					  =				(
																    encoded_inputs["global_attention_mask"] + [-1] * difference
																)
													elif self.padding_side == "left":
																lowercase__					  =				[-1] * difference + encoded_inputs[
																    "global_attention_mask"
																]
													else:
																raise ValueError("Invalid padding strategy:" + str(self.padding_side						)						)

							return encoded_inputs




