



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

from utils import logger
class 				_snake_case			(					lowercase__):




				def __init__(						self					:						List[str],					__lowercase					:						Union[str, Any],					__lowercase					:						Dict						):
							lowercase__					  =				params

							lowercase__					  =				np.array(__lowercase						)
							lowercase__					  =				np.array([len(__lowercase						) 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					:						str,					__lowercase					:						List[str]						):
							return (self.token_ids[index], self.lengths[index])




				def __len__(						self					:						str						):
							return len(self.lengths						)




				def 							A__      (						self					:						Optional[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 							A__      (						self					:						Union[str, Any]						):
							lowercase__					  =				self.params.max_model_input_size
							lowercase__					  =				self.lengths > max_len
							logger.info(F'''Splitting {sum(__lowercase						)} too long sequences.'''						)

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

							lowercase__					  =				[]
							lowercase__					  =				[]
							if self.params.mlm:
										lowercase__  ,       lowercase__					  =				self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
							else:
										lowercase__  ,       lowercase__					  =				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:
													lowercase__					  =				[]
													for sub_s in divide_chunks(seq_,					max_len - 2						):
																if sub_s[0] != cls_id:
																			lowercase__					  =				np.insert(__lowercase,					0,					__lowercase						)
																if sub_s[-1] != sep_id:
																			lowercase__					  =				np.insert(__lowercase,					len(__lowercase						),					__lowercase						)
																assert len(__lowercase						) <= max_len
																assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
																sub_seqs.append(__lowercase						)

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

							lowercase__					  =				np.array(__lowercase						)
							lowercase__					  =				np.array(__lowercase						)




				def 							A__      (						self					:						Tuple						):
							lowercase__					  =				len(self						)
							lowercase__					  =				self.lengths > 11
							lowercase__					  =				self.token_ids[indices]
							lowercase__					  =				self.lengths[indices]
							lowercase__					  =				len(self						)
							logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.'''						)




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




				def 							A__      (						self					:						Tuple						):
							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 							A__      (						self					:						List[Any],					__lowercase					:						Optional[int]						):
							lowercase__					  =				[t[0] for t in batch]
							lowercase__					  =				[t[1] for t in batch]
							assert len(__lowercase						) == len(__lowercase						)

							# Max for paddings
							lowercase__					  =				max(__lowercase						)

							# Pad token ids
							if self.params.mlm:
										lowercase__					  =				self.params.special_tok_ids["pad_token"]
							else:
										lowercase__					  =				self.params.special_tok_ids["unk_token"]
							lowercase__					  =				[list(t.astype(__lowercase						)						) + [pad_idx] * (max_seq_len_ - len(__lowercase						)) for t in token_ids]
							assert len(tk_						) == len(__lowercase						)
							assert all(len(__lowercase						) == max_seq_len_ for t in tk_						)

							lowercase__					  =				torch.tensor(tk_						)  # (bs, max_seq_len_)
							lowercase__					  =				torch.tensor(__lowercase						)  # (bs)
							return tk_t, lg_t









import math



def __lowerCAmelCase (     SCREAMING_SNAKE_CASE_						,  SCREAMING_SNAKE_CASE_  ):
			if 0 not in (x, y):
						# We use the relation x^y = y*log10(x), where 10 is the base.
						return y * math.logaa(SCREAMING_SNAKE_CASE_  )
			else:
						if x == 0:  # 0 raised to any number is 0
									return 0
						elif y == 0:
									return 1  # any number raised to 0 is 1
			raise AssertionError("This should never happen"  )


if __name__ == "__main__":  # Main function
							# Read two numbers from input and typecast them to int using map function.
							# Here x is the base and y is the power.
							lowercase_								=     """Enter the base and the power separated by a comma: """
							lowercase_				,    lowercase_								=     map(int, input(prompt).split(""","""))
							lowercase_				,    lowercase_								=     map(int, input(prompt).split(""","""))

							# We find the log of each number, using the function res(), which takes two
							# arguments.
							lowercase_								=     res(xa, ya)
							lowercase_								=     res(xa, ya)

							# We check for the largest number
							if resa > resa:
														print("""Largest number is""", xa, """^""", ya)
							elif resa > resa:
														print("""Largest number is""", xa, """^""", ya)
							else:
														print("""Both are equal""")




