
'''simple docstring'''
import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_longformer import LongformerTokenizer


lowercase__						=			logging.get_logger(__name__)

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

lowercase__						=			{
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "allenai/longformer-base-4096": (
            "https://huggingface.co/allenai/longformer-base-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/tokenizer.json"
        ),
    },
}

lowercase__						=			{
    "allenai/longformer-base-4096": 4_0_9_6,
    "allenai/longformer-large-4096": 4_0_9_6,
    "allenai/longformer-large-4096-finetuned-triviaqa": 4_0_9_6,
    "allenai/longformer-base-4096-extra.pos.embd.only": 4_0_9_6,
    "allenai/longformer-large-4096-extra.pos.embd.only": 4_0_9_6,
}






class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		lowerCamelCase           =	VOCAB_FILES_NAMES
		lowerCamelCase           =	PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase           =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase           =	["""input_ids""", """attention_mask"""]
		lowerCamelCase           =	LongformerTokenizer



		def __init__(			self							: Union[str, Any]     ,  UpperCamelCase__							: List[Any]=None     ,  UpperCamelCase__							: List[Any]=None     ,  UpperCamelCase__							: List[Any]=None     ,  UpperCamelCase__							: Union[str, Any]="replace"     ,  UpperCamelCase__							: Optional[Any]="<s>"     ,  UpperCamelCase__							: Dict="</s>"     ,  UpperCamelCase__							: Optional[Any]="</s>"     ,  UpperCamelCase__							: Optional[int]="<s>"     ,  UpperCamelCase__							: Dict="<unk>"     ,  UpperCamelCase__							: int="<pad>"     ,  UpperCamelCase__							: Optional[Any]="<mask>"     ,  UpperCamelCase__							: int=False     ,  UpperCamelCase__							: int=True     ,  **UpperCamelCase__							: List[Any]     ,  )	-> Any:






				"""simple docstring"""







				super().__init__(
				    UpperCamelCase__     ,  UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  errors=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  add_prefix_space=UpperCamelCase__     ,  trim_offsets=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

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

				snake_case     :					int     =	add_prefix_space

				snake_case     :					Union[str, Any]     =	'''post_processor'''
				snake_case     :					Dict     =	getattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__			)
				if tokenizer_component_instance:
						snake_case     :					str     =	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:
								snake_case     :					Dict     =	tuple(state['''sep''']			)
						if "cls" in state:
								snake_case     :					Optional[int]     =	tuple(state['''cls''']			)

						snake_case     :					Any     =	False

						if state.get('''add_prefix_space'''     ,  UpperCamelCase__			) != add_prefix_space:
								snake_case     :					Optional[Any]     =	add_prefix_space
								snake_case     :					Optional[int]     =	True

						if state.get('''trim_offsets'''     ,  UpperCamelCase__			) != trim_offsets:
								snake_case     :					List[Any]     =	trim_offsets
								snake_case     :					Tuple     =	True

						if changes_to_apply:
								snake_case     :					Any     =	getattr(UpperCamelCase__     ,  state.pop('''type'''			)			)
								snake_case     :					str     =	component_class(**UpperCamelCase__			)
								setattr(self.backend_tokenizer     ,  UpperCamelCase__     ,  UpperCamelCase__			)



		@property
		def 			lowerCAmelCase							(			self							: int			)	-> str:






				"""simple docstring"""







				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 			lowerCAmelCase							(			self							: Optional[Any]     ,  UpperCamelCase__							: Union[str, Any]			)	-> List[str]:






				"""simple docstring"""







				snake_case     :					Any     =	AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__			) if isinstance(UpperCamelCase__     ,  UpperCamelCase__			) else value
				snake_case     :					Optional[Any]     =	value



		def 			lowerCAmelCase							(			self							: Tuple     ,  *UpperCamelCase__							: Optional[Any]     ,  **UpperCamelCase__							: List[str]			)	-> BatchEncoding:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	kwargs.get('''is_split_into_words'''     ,  UpperCamelCase__			)
				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(*UpperCamelCase__     ,  **UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Dict     ,  *UpperCamelCase__							: List[Any]     ,  **UpperCamelCase__							: int			)	-> BatchEncoding:






				"""simple docstring"""







				snake_case     :					Dict     =	kwargs.get('''is_split_into_words'''     ,  UpperCamelCase__			)

				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(*UpperCamelCase__     ,  **UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Optional[Any]     ,  UpperCamelCase__							: str     ,  UpperCamelCase__							: Optional[str] = None			)	-> Tuple[str]:






				"""simple docstring"""







				snake_case     :					str     =	self._tokenizer.model.save(UpperCamelCase__     ,  name=UpperCamelCase__			)
				return tuple(UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: Union[str, Any]     ,  UpperCamelCase__							: int     ,  UpperCamelCase__							: List[Any]=None			)	-> str:






				"""simple docstring"""







				snake_case     :					int     =	[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 			lowerCAmelCase							(			self							: str     ,  UpperCamelCase__							: List[int]     ,  UpperCamelCase__							: Optional[List[int]] = None			)	-> List[int]:






				"""simple docstring"""







				snake_case     :					Union[str, Any]     =	[self.sep_token_id]
				snake_case     :					int     =	[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]




'''simple docstring'''
import numpy as np




def       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__ )     ->       Optional[Any]:


		'''simple docstring'''


		snake_case ,snake_case     :					str     =	a.shape
		snake_case     :					Union[str, Any]     =	min(SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )
		snake_case     :					List[Any]     =	np.eye(SCREAMING_SNAKE_CASE__ )
		snake_case     :					Union[str, Any]     =	a.copy()

		for k in range(t - 1 ):
				# select a column of modified matrix A':
				snake_case     :					Union[str, Any]     =	r[k:, [k]]
				# construct first basis vector
				snake_case     :					str     =	np.zeros_like(SCREAMING_SNAKE_CASE__ )
				snake_case     :					List[str]     =	1.0
				# determine scaling factor
				snake_case     :					int     =	np.linalg.norm(SCREAMING_SNAKE_CASE__ )
				# construct vector v for Householder reflection
				snake_case     :					Optional[int]     =	x + np.sign(x[0] ) * alpha * ea
				v /= np.linalg.norm(SCREAMING_SNAKE_CASE__ )

				# construct the Householder matrix
				snake_case     :					Tuple     =	np.eye(m - k ) - 2.0 * v @ v.T
				# pad with ones and zeros as necessary
				snake_case     :					int     =	np.block([[np.eye(SCREAMING_SNAKE_CASE__ ), np.zeros((k, m - k) )], [np.zeros((m - k, k) ), q_k]] )

				snake_case     :					int     =	q @ q_k.T
				snake_case     :					Dict     =	q_k @ r

		return q, r


if __name__ == "__main__":
						import doctest

						doctest.testmod()


