



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		snake_case    :						Any  =						None


snake_case    :						Optional[int]  =						logging.get_logger(__name__)


snake_case    :						Optional[Any]  =						'▁'

snake_case    :						Any  =						{'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

snake_case    :						List[str]  =						{
    'vocab_file': {'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'},
    'tokenizer_file': {
        'google/pegasus-xsum': 'https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'
    },
}

snake_case    :						Tuple  =						{
    'google/pegasus-xsum': 5_1_2,
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Any           =						VOCAB_FILES_NAMES
					__A     :    Tuple           =						PRETRAINED_VOCAB_FILES_MAP
					__A     :    List[Any]           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					__A     :    Dict           =						PegasusTokenizer
					__A     :    Optional[int]           =						['input_ids', 'attention_mask']







					def __init__(       self      :     Optional[Any]			,		_A      :     Union[str, Any]=None			,		_A      :     Any=None			,		_A      :     List[str]="<pad>"			,		_A      :     Optional[int]="</s>"			,		_A      :     List[str]="<unk>"			,		_A      :     Tuple="<mask_2>"			,		_A      :     Tuple="<mask_1>"			,		_A      :     Any=None			,		_A      :     str=103			,		**_A      :     List[Any]			,		):
											A__	:							List[Any]						    = offset

											if additional_special_tokens is not None:
																	if not isinstance(_A			,		_A):
																							raise TypeError(
																							    F'additional_special_tokens should be of type {type(_A)}, but is'
																							    F' {type(_A)}')

																	A__	:							Tuple						    = (
																	    ([mask_token_sent] + additional_special_tokens)
																	    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																	    else additional_special_tokens
																	)
																	# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																	additional_special_tokens_extended += [
																	    F'<unk_{i}>' for i in range(len(_A)			,		self.offset - 1)
																	]

																	if len(set(_A)) != len(_A):
																							raise ValueError(
																							    "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
																							    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.')
																	A__	:							Tuple						    = additional_special_tokens_extended
											else:
																	A__	:							Optional[Any]						    = [mask_token_sent] if mask_token_sent is not None else []
																	additional_special_tokens += [F'<unk_{i}>' for i in range(2			,		self.offset)]

											super().__init__(
											    _A			,		tokenizer_file=_A			,		pad_token=_A			,		eos_token=_A			,		unk_token=_A			,		mask_token=_A			,		mask_token_sent=_A			,		offset=_A			,		additional_special_tokens=_A			,		**_A			,		)
											A__	:							Dict						    = vocab_file
											A__	:							Dict						    = False if not self.vocab_file else True







					def  _lowercase			(       self      :     Tuple			,		_A      :     Union[str, Any]):
											A__	:							str						    = set(self.all_special_ids)  # call it once instead of inside list comp
											all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

											if all_special_ids != set(range(len(self.additional_special_tokens) + 3)):
																	raise ValueError(
																	    "There should be 3 special tokens: mask_token, pad_token, and eos_token +"
																	    F' {len(self.additional_special_tokens)} additional_special_tokens, but got {all_special_ids}')

											return [1 if x in all_special_ids else 0 for x in seq]







					def  _lowercase			(       self      :     Tuple			,		_A      :     List			,		_A      :     Optional[List] = None			,		_A      :     bool = False):
											if already_has_special_tokens:
																	return self._special_token_mask(_A)
											elif token_ids_a is None:
																	return self._special_token_mask(_A) + [1]
											else:
																	return self._special_token_mask(token_ids_a + token_ids_a) + [1]







					def  _lowercase			(       self      :     List[str]			,		_A      :     str			,		_A      :     Optional[Any]=None):
											if token_ids_a is None:
																	return token_ids_a + [self.eos_token_id]
											# We don't expect to process pairs, but leave the pair logic for API consistency
											return token_ids_a + token_ids_a + [self.eos_token_id]







					def  _lowercase			(       self      :     int			,		_A      :     str			,		_A      :     Optional[str] = None):
											if not self.can_save_slow_tokenizer:
																	raise ValueError(
																	    "Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
																	    "tokenizer.")

											if not os.path.isdir(_A):
																	logger.error(F'Vocabulary path ({save_directory}) should be a directory')
																	return
											A__	:							str						    = os.path.join(
											    _A			,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

											if os.path.abspath(self.vocab_file) != os.path.abspath(_A):
																	copyfile(self.vocab_file			,		_A)

											return (out_vocab_file,)




from collections.abc import Callable

import numpy as np
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase							,     __lowercase							,     __lowercase       )		->		np.array:







						"""simple docstring"""

						A__	:							Any						    = int(np.ceil((x_end - xa) / step_size       )       )
						A__	:							Union[str, Any]						    = np.zeros((n + 1,)       )
						A__	:							Any						    = ya
						A__	:							Union[str, Any]						    = xa

						for k in range(__lowercase       ):
												A__	:							Any						    = y[k] + step_size * ode_func(__lowercase							,     y[k]       )
												A__	:							Any						    = y[k] + (
												    (step_size / 2) * (ode_func(__lowercase							,     y[k]       ) + ode_func(x + step_size							,     __lowercase       ))
												)
												x += step_size

						return y


if __name__ == "__main__":
		import doctest

		doctest.testmod()