






'''simple docstring'''







import os
from math import logaa





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase = "base_exp.txt"   )   -> int:
       lowerCamelCase__      :			str 						= 0
       lowerCamelCase__      :			str 						= 0
       for i, line in enumerate(open(os.path.join(os.path.dirname(lowerCamelCase__   )       ,    lowerCamelCase__   )   )   ):
              lowerCamelCase__						,	lowerCamelCase__      :			Any 						= list(map(lowerCamelCase__       ,    line.split(""","""   )   )   )
              if x * logaa(lowerCamelCase__   ) > largest:
                     lowerCamelCase__      :			Dict 						= x * logaa(lowerCamelCase__   )
                     lowerCamelCase__      :			Dict 						= i + 1
       return result


if __name__ == "__main__":
   print(solution())









'''simple docstring'''







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

from ..utils.generic import ModelOutput

class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Optional[int] 						= dataset
														lowerCamelCase__      :			Optional[int] 						= process
														lowerCamelCase__      :			List[str] 						= params




							def __len__(							self:       List[str]  ):
														return len(self.dataset  )




							def __getitem__(							self:       Any    ,							UpperCamelCase__:       int  ):
														lowerCamelCase__      :			Dict 						= self.dataset[i]
														lowerCamelCase__      :			Union[str, Any] 						= self.process(UpperCamelCase__    ,							**self.params  )
														return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       Any=None  ):
														lowerCamelCase__      :			int 						= loader
														lowerCamelCase__      :			str 						= infer
														lowerCamelCase__      :			Optional[int] 						= params
														if loader_batch_size == 1:
																					# Let's spare some time by deactivating altogether
																					lowerCamelCase__      :			Optional[int] 						= None
														lowerCamelCase__      :			int 						= loader_batch_size

														# Internal bookkeeping
														lowerCamelCase__      :			Optional[Any] 						= None
														lowerCamelCase__      :			Optional[Any] 						= None




							def __len__(							self:       Dict  ):
														return len(self.loader  )




							def __iter__(							self:       Optional[int]  ):
														lowerCamelCase__      :			List[Any] 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Any  ):
														if isinstance(self._loader_batch_data    ,							torch.Tensor  ):
																					# Batch data is simple tensor, just fetch the slice
																					lowerCamelCase__      :			str 						= self._loader_batch_data[self._loader_batch_index]
														else:
																					# Batch data is assumed to be BaseModelOutput (or dict)
																					lowerCamelCase__      :			int 						= {}
																					for k, element in self._loader_batch_data.items():
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Convert ModelOutput to tuple first
																																			lowerCamelCase__      :			str 						= element.to_tuple()
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			Tuple 						= tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																			elif isinstance(element[0]    ,							np.ndarray  ):
																																										lowerCamelCase__      :			str 						= tuple(np.expand_dims(el[self._loader_batch_index]    ,							0  ) for el in element  )
																																			continue
																												if k in {"hidden_states", "past_key_values", "attentions"} and isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			# Those are stored as lists of tensors so need specific unbatching.
																																			if isinstance(element[0]    ,							torch.Tensor  ):
																																										lowerCamelCase__      :			List[str] 						= tuple(el[self._loader_batch_index].unsqueeze(0  ) for el in element  )
																																			elif isinstance(element[0]    ,							np.ndarray  ):
																																										lowerCamelCase__      :			int 						= tuple(np.expand_dims(el[self._loader_batch_index]    ,							0  ) for el in element  )
																																			continue
																												if element is None:
																																			# This can happen for optional data that get passed around
																																			lowerCamelCase__      :			List[str] 						= None
																												elif isinstance(element[self._loader_batch_index]    ,							torch.Tensor  ):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers

																																			lowerCamelCase__      :			Optional[Any] 						= element[self._loader_batch_index].unsqueeze(0  )
																												elif isinstance(element[self._loader_batch_index]    ,							np.ndarray  ):
																																			# Take correct batch data, but make it looked like batch_size=1
																																			# For compatibility with other methods within transformers
																																			lowerCamelCase__      :			int 						= np.expand_dims(element[self._loader_batch_index]    ,							0  )
																												else:
																																			# This is typically a list, so no need to `unsqueeze`.
																																			lowerCamelCase__      :			str 						= element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
																					lowerCamelCase__      :			Optional[int] 						= self._loader_batch_data.__class__(UpperCamelCase__  )
														self._loader_batch_index += 1
														return result




							def 	lowerCamelCase_       (							self:       List[Any]  ):
														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					# We are currently unrolling a batch so we just need to return
																					# the current item within a batch
																					return self.loader_batch_item()

														# We're out of items within a batch
														lowerCamelCase__      :			Optional[Any] 						= next(self.iterator  )
														lowerCamelCase__      :			List[str] 						= self.infer(UpperCamelCase__    ,							**self.params  )
														# We now have a batch of "inferred things".
														if self.loader_batch_size is not None:
																					# Try to infer the size of the batch
																					if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																												lowerCamelCase__      :			Optional[Any] 						= processed
																					else:
																												lowerCamelCase__      :			Union[str, Any] 						= list(processed.keys()  )[0]
																												lowerCamelCase__      :			Any 						= processed[key]
																					if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																												lowerCamelCase__      :			Any 						= len(UpperCamelCase__  )
																					else:
																												lowerCamelCase__      :			List[str] 						= first_tensor.shape[0]
																					if 0 < observed_batch_size < self.loader_batch_size:
																												# could be last batch so we can't unroll as many
																												# elements.
																												lowerCamelCase__      :			Union[str, Any] 						= observed_batch_size
																					# Setting internal index to unwrap the batch
																					lowerCamelCase__      :			List[Any] 						= processed
																					lowerCamelCase__      :			List[Any] 						= 0
																					return self.loader_batch_item()
														else:
																					# We're not unrolling batches
																					return processed



class _lowercase			(       _lowercase	):




							def __init__(							self:       List[str]    ,							UpperCamelCase__:       Any    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       List[Any]=None  ):
														super().__init__(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )




							def __iter__(							self:       Union[str, Any]  ):
														lowerCamelCase__      :			str 						= iter(self.loader  )
														lowerCamelCase__      :			int 						= None
														return self




							def 	lowerCamelCase_       (							self:       str  ):
														if self.subiterator is None:
																					lowerCamelCase__      :			Optional[Any] 						= self.infer(next(self.iterator  )    ,							**self.params  )
														try:
																					# Try to return next item
																					lowerCamelCase__      :			Tuple 						= next(self.subiterator  )
														except StopIteration:
																					# When a preprocess iterator ends, we can start lookig at the next item
																					# ChunkIterator will keep feeding until ALL elements of iterator
																					# all have created their subiterator and have been iterating against.
																					#
																					# Another way to look at it, is we're basically flattening lists of lists
																					# into a single list, but with generators
																					lowerCamelCase__      :			Any 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					lowerCamelCase__      :			Union[str, Any] 						= next(self.subiterator  )
														return processed



class _lowercase			(       _lowercase	):




							def __iter__(							self:       List[Any]  ):
														lowerCamelCase__      :			int 						= iter(self.loader  )
														return self




							def 	lowerCamelCase_       (							self:       Tuple  ):
														# Extremely similar to PipelineIterator in its unpacking mechanism
														# BUT, we have an extra required item which is the presence of `is_last`
														# That is because everything is flattened by `PipelineChunkIterator` we
														# need to keep track of how to regroup here in the original `process`
														# boundaries so that `process` and `postprocess` see the same data.

														# This iterator accumulates items (possibly while unbatching) until it
														# its a `is_last` and then just passes it on to the caller.
														lowerCamelCase__      :			List[str] 						= False
														lowerCamelCase__      :			Union[str, Any] 						= []
														if self._loader_batch_index is not None and self._loader_batch_index < self.loader_batch_size:
																					while self._loader_batch_index < self.loader_batch_size:
																												lowerCamelCase__      :			Any 						= self.loader_batch_item()
																												lowerCamelCase__      :			Tuple 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
																												if is_last:
																																			return accumulator

														while not is_last:
																					lowerCamelCase__      :			str 						= self.infer(next(self.iterator  )    ,							**self.params  )
																					if self.loader_batch_size is not None:
																												if isinstance(UpperCamelCase__    ,							torch.Tensor  ):
																																			lowerCamelCase__      :			Dict 						= processed
																												else:
																																			lowerCamelCase__      :			Dict 						= list(processed.keys()  )[0]
																																			lowerCamelCase__      :			Dict 						= processed[key]
																												if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																																			lowerCamelCase__      :			List[Any] 						= len(UpperCamelCase__  )
																												else:
																																			lowerCamelCase__      :			Dict 						= first_tensor.shape[0]
																												if 0 < observed_batch_size < self.loader_batch_size:
																																			# could be last batch so we can't unroll as many
																																			# elements.
																																			lowerCamelCase__      :			str 						= observed_batch_size
																												lowerCamelCase__      :			str 						= processed
																												lowerCamelCase__      :			Optional[int] 						= 0
																												while self._loader_batch_index < self.loader_batch_size:
																																			lowerCamelCase__      :			List[Any] 						= self.loader_batch_item()
																																			lowerCamelCase__      :			str 						= item.pop("""is_last"""  )
																																			accumulator.append(UpperCamelCase__  )
																																			if is_last:
																																										return accumulator
																					else:
																												lowerCamelCase__      :			Optional[Any] 						= processed
																												lowerCamelCase__      :			Optional[int] 						= item.pop("""is_last"""  )
																												accumulator.append(UpperCamelCase__  )
														return accumulator



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			Union[str, Any] 						= dataset
														lowerCamelCase__      :			str 						= key




							def __len__(							self:       Optional[Any]  ):
														return len(self.dataset  )




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Any  ):
														return self.dataset[i][self.key]



class _lowercase			(       _lowercase	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dataset    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       str  ):
														lowerCamelCase__      :			str 						= dataset
														lowerCamelCase__      :			Dict 						= keya
														lowerCamelCase__      :			List[str] 						= keya




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




							def __getitem__(							self:       List[str]    ,							UpperCamelCase__:       Union[str, Any]  ):
														return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}

