'''simple docstring'''


import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger






class 						A   (   UpperCAmelCase   ):





							def __init__(      self				:							int     ,		__a				:							int     ,		__a				:							str	)							->	List[str]:
								__UpperCAmelCase							=			params

								__UpperCAmelCase							=			np.array(__a	)
								__UpperCAmelCase							=			np.array([len(__a	) for t in data]	)

								self.check()
								self.remove_long_sequences()
								self.remove_empty_sequences()
								self.remove_unknown_sequences()
								self.check()
								self.print_statistics()





							def __getitem__(      self				:							List[str]     ,		__a				:							Optional[int]	)							->	Dict:
								return (self.token_ids[index], self.lengths[index])





							def __len__(      self				:							int	)							->	Union[str, Any]:
								return len(self.lengths	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Union[str, Any]:
								assert len(self.token_ids	) == len(self.lengths	)
								assert all(self.lengths[i] == len(self.token_ids[i]	) for i in range(len(self.lengths	)	)	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Optional[Any]:
								__UpperCAmelCase							=			self.params.max_model_input_size
								__UpperCAmelCase							=			self.lengths > max_len
								logger.info(f"""Splitting {sum(__a	)} too long sequences."""	)

								def divide_chunks(__a				:							List[Any]     ,		__a				:							List[str]	):
									return [l[i : i + n] for i in range(0     ,		len(__a	)     ,		__a	)]

								__UpperCAmelCase							=			[]
								__UpperCAmelCase							=			[]
								if self.params.mlm:
									__UpperCAmelCase				,			__UpperCAmelCase							=			self.params.special_tok_ids['''cls_token'''], self.params.special_tok_ids['''sep_token''']
								else:
									__UpperCAmelCase				,			__UpperCAmelCase							=			self.params.special_tok_ids['''bos_token'''], self.params.special_tok_ids['''eos_token''']

								for seq_, len_ in zip(self.token_ids     ,		self.lengths	):
									assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
									if len_ <= max_len:
										new_tok_ids.append(seq_	)
										new_lengths.append(len_	)
									else:
										__UpperCAmelCase							=			[]
										for sub_s in divide_chunks(seq_     ,		max_len - 2	):
											if sub_s[0] != cls_id:
												__UpperCAmelCase							=			np.insert(__a     ,		0     ,		__a	)
											if sub_s[-1] != sep_id:
												__UpperCAmelCase							=			np.insert(__a     ,		len(__a	)     ,		__a	)
											assert len(__a	) <= max_len
											assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
											sub_seqs.append(__a	)

										new_tok_ids.extend(__a	)
										new_lengths.extend([len(__a	) for l in sub_seqs]	)

								__UpperCAmelCase							=			np.array(__a	)
								__UpperCAmelCase							=			np.array(__a	)





							def       snake_case__						(      self				:							Any	)							->	Dict:
								__UpperCAmelCase							=			len(self	)
								__UpperCAmelCase							=			self.lengths > 1_1
								__UpperCAmelCase							=			self.token_ids[indices]
								__UpperCAmelCase							=			self.lengths[indices]
								__UpperCAmelCase							=			len(self	)
								logger.info(f"""Remove {init_size - new_size} too short (<=11 tokens) sequences."""	)





							def       snake_case__						(      self				:							Tuple	)							->	Any:
								if "unk_token" not in self.params.special_tok_ids:
									return
								else:
									__UpperCAmelCase							=			self.params.special_tok_ids['''unk_token''']
								__UpperCAmelCase							=			len(self	)
								__UpperCAmelCase							=			np.array([np.count_nonzero(a == unk_token_id	) for a in self.token_ids]	)
								__UpperCAmelCase							=			(unk_occs / self.lengths) < 0.5
								__UpperCAmelCase							=			self.token_ids[indices]
								__UpperCAmelCase							=			self.lengths[indices]
								__UpperCAmelCase							=			len(self	)
								logger.info(f"""Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."""	)





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Optional[Any]:
								if not self.params.is_master:
									return
								logger.info(f"""{len(self	)} sequences"""	)
								# data_len = sum(self.lengths)
								# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
								# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

								# unk_idx = self.params.special_tok_ids['unk_token']
								# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
								# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')






							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]	)							->	Dict:
								__UpperCAmelCase							=			[t[0] for t in batch]
								__UpperCAmelCase							=			[t[1] for t in batch]
								assert len(__a	) == len(__a	)

								# Max for paddings
								__UpperCAmelCase							=			max(__a	)

								# Pad token ids
								if self.params.mlm:
									__UpperCAmelCase							=			self.params.special_tok_ids['''pad_token''']
								else:
									__UpperCAmelCase							=			self.params.special_tok_ids['''unk_token''']
								__UpperCAmelCase							=			[list(t.astype(__a	)	) + [pad_idx] * (max_seq_len_ - len(__a	)) for t in token_ids]
								assert len(tk_	) == len(__a	)
								assert all(len(__a	) == max_seq_len_ for t in tk_	)

								__UpperCAmelCase							=			torch.tensor(tk_	)  # (bs, max_seq_len_)
								__UpperCAmelCase							=			torch.tensor(__a	)  # (bs)
								return tk_t, lg_t

'''simple docstring'''


from __future__ import annotations

import math





def       lowerCAmelCase					(		UpperCamelCase__				:   float    ,					UpperCamelCase__				:   int       ):


	"""simple docstring"""
	__UpperCAmelCase							=			u
	for i in range(1    ,					UpperCamelCase__       ):
		__UpperCAmelCase							=			temp * (u - i)
	return temp





def       lowerCAmelCase					(		):


	"""simple docstring"""
	__UpperCAmelCase							=			int(input('''enter the numbers of values: '''       )       )
	__UpperCAmelCase							=			[]
	for _ in range(UpperCamelCase__       ):
		y.append([]       )
	for i in range(UpperCamelCase__       ):
		for j in range(UpperCamelCase__       ):
			y[i].append(UpperCamelCase__       )
			__UpperCAmelCase							=			0

	print('''enter the values of parameters in a list: '''       )
	__UpperCAmelCase							=			list(map(UpperCamelCase__    ,					input().split()       )       )

	print('''enter the values of corresponding parameters: '''       )
	for i in range(UpperCamelCase__       ):
		__UpperCAmelCase							=			float(input()       )

	__UpperCAmelCase							=			int(input('''enter the value to interpolate: '''       )       )
	__UpperCAmelCase							=			(value - x[0]) / (x[1] - x[0])

	# for calculating forward difference table

	for i in range(1    ,					UpperCamelCase__       ):
		for j in range(n - i       ):
			__UpperCAmelCase							=			y[j + 1][i - 1] - y[j][i - 1]

	__UpperCAmelCase							=			y[0][0]
	for i in range(1    ,					UpperCamelCase__       ):
		summ += (ucal(UpperCamelCase__    ,					UpperCamelCase__       ) * y[0][i]) / math.factorial(UpperCamelCase__       )

	print(f"""the value at {value} is {summ}"""       )


if __name__ == "__main__":
					main()
