






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

from ..utils.generic import ModelOutput

class 		_snake_case	(	A__       ):






			def __init__(							self		,       a		,       a		,       a)   ->							str:
						SCREAMING_SNAKE_CASE							=			dataset
						SCREAMING_SNAKE_CASE							=			process
						SCREAMING_SNAKE_CASE							=			params






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







			def __getitem__(							self		,       a)   ->							int:
						SCREAMING_SNAKE_CASE							=			self.dataset[i]
						SCREAMING_SNAKE_CASE							=			self.process(lowerCamelCase__		,       **self.params)
						return processed



class 		_snake_case	(	A__       ):






			def __init__(							self		,       a		,       a		,       a		,       a=None)   ->							int:
						SCREAMING_SNAKE_CASE							=			loader
						SCREAMING_SNAKE_CASE							=			infer
						SCREAMING_SNAKE_CASE							=			params
						if loader_batch_size == 1:
									# Let's spare some time by deactivating altogether
									SCREAMING_SNAKE_CASE							=			None
						SCREAMING_SNAKE_CASE							=			loader_batch_size

						# Internal bookkeeping
						SCREAMING_SNAKE_CASE							=			None
						SCREAMING_SNAKE_CASE							=			None






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






			def __iter__(							self)   ->							List[str]:
						SCREAMING_SNAKE_CASE							=			iter(self.loader)
						return self






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						if isinstance(self._loader_batch_data		,       torch.Tensor):
									# Batch data is simple tensor, just fetch the slice
									SCREAMING_SNAKE_CASE							=			self._loader_batch_data[self._loader_batch_index]
						else:
									# Batch data is assumed to be BaseModelOutput (or dict)
									SCREAMING_SNAKE_CASE							=			{}
									for k, element in self._loader_batch_data.items():
												if isinstance(lowerCamelCase__		,       lowerCamelCase__):
															# Convert ModelOutput to tuple first
															SCREAMING_SNAKE_CASE							=			element.to_tuple()
															if isinstance(element[0]		,       torch.Tensor):
																		SCREAMING_SNAKE_CASE							=			tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
															elif isinstance(element[0]		,       np.ndarray):
																		SCREAMING_SNAKE_CASE							=			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(lowerCamelCase__		,       lowerCamelCase__):
															# Those are stored as lists of tensors so need specific unbatching.
															if isinstance(element[0]		,       torch.Tensor):
																		SCREAMING_SNAKE_CASE							=			tuple(el[self._loader_batch_index].unsqueeze(0) for el in element)
															elif isinstance(element[0]		,       np.ndarray):
																		SCREAMING_SNAKE_CASE							=			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
															SCREAMING_SNAKE_CASE							=			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

															SCREAMING_SNAKE_CASE							=			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
															SCREAMING_SNAKE_CASE							=			np.expand_dims(element[self._loader_batch_index]		,       0)
												else:
															# This is typically a list, so no need to `unsqueeze`.
															SCREAMING_SNAKE_CASE							=			element[self._loader_batch_index]
            # Recreate the element by reusing the original class to make it look
            # batch_size=1
									SCREAMING_SNAKE_CASE							=			self._loader_batch_data.__class__(lowerCamelCase__)
						self._loader_batch_index += 1
						return result







			def  SCREAMING_SNAKE_CASE__     (							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
						SCREAMING_SNAKE_CASE							=			next(self.iterator)
						SCREAMING_SNAKE_CASE							=			self.infer(lowerCamelCase__		,       **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(lowerCamelCase__		,       torch.Tensor):
												SCREAMING_SNAKE_CASE							=			processed
									else:
												SCREAMING_SNAKE_CASE							=			list(processed.keys())[0]
												SCREAMING_SNAKE_CASE							=			processed[key]
									if isinstance(lowerCamelCase__		,       lowerCamelCase__):
												SCREAMING_SNAKE_CASE							=			len(lowerCamelCase__)
									else:
												SCREAMING_SNAKE_CASE							=			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.
												SCREAMING_SNAKE_CASE							=			observed_batch_size
									# Setting internal index to unwrap the batch
									SCREAMING_SNAKE_CASE							=			processed
									SCREAMING_SNAKE_CASE							=			0
									return self.loader_batch_item()
						else:
									# We're not unrolling batches
									return processed



class 		_snake_case	(	A__       ):






			def __init__(							self		,       a		,       a		,       a		,       a=None)   ->							Optional[int]:
						super().__init__(lowerCamelCase__		,       lowerCamelCase__		,       lowerCamelCase__)






			def __iter__(							self)   ->							Dict:
						SCREAMING_SNAKE_CASE							=			iter(self.loader)
						SCREAMING_SNAKE_CASE							=			None
						return self







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						if self.subiterator is None:
									SCREAMING_SNAKE_CASE							=			self.infer(next(self.iterator)		,       **self.params)
						try:
									# Try to return next item
									SCREAMING_SNAKE_CASE							=			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
									SCREAMING_SNAKE_CASE							=			self.infer(next(self.iterator)		,       **self.params)
									SCREAMING_SNAKE_CASE							=			next(self.subiterator)
						return processed



class 		_snake_case	(	A__       ):






			def __iter__(							self)   ->							Tuple:
						SCREAMING_SNAKE_CASE							=			iter(self.loader)
						return self







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						SCREAMING_SNAKE_CASE							=			False
						SCREAMING_SNAKE_CASE							=			[]
						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:
												SCREAMING_SNAKE_CASE							=			self.loader_batch_item()
												SCREAMING_SNAKE_CASE							=			item.pop('is_last')
												accumulator.append(lowerCamelCase__)
												if is_last:
															return accumulator

						while not is_last:
									SCREAMING_SNAKE_CASE							=			self.infer(next(self.iterator)		,       **self.params)
									if self.loader_batch_size is not None:
												if isinstance(lowerCamelCase__		,       torch.Tensor):
															SCREAMING_SNAKE_CASE							=			processed
												else:
															SCREAMING_SNAKE_CASE							=			list(processed.keys())[0]
															SCREAMING_SNAKE_CASE							=			processed[key]
												if isinstance(lowerCamelCase__		,       lowerCamelCase__):
															SCREAMING_SNAKE_CASE							=			len(lowerCamelCase__)
												else:
															SCREAMING_SNAKE_CASE							=			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.
															SCREAMING_SNAKE_CASE							=			observed_batch_size
												SCREAMING_SNAKE_CASE							=			processed
												SCREAMING_SNAKE_CASE							=			0
												while self._loader_batch_index < self.loader_batch_size:
															SCREAMING_SNAKE_CASE							=			self.loader_batch_item()
															SCREAMING_SNAKE_CASE							=			item.pop('is_last')
															accumulator.append(lowerCamelCase__)
															if is_last:
																		return accumulator
									else:
												SCREAMING_SNAKE_CASE							=			processed
												SCREAMING_SNAKE_CASE							=			item.pop('is_last')
												accumulator.append(lowerCamelCase__)
						return accumulator



class 		_snake_case	(	A__       ):






			def __init__(							self		,       a		,       a)   ->							str:
						SCREAMING_SNAKE_CASE							=			dataset
						SCREAMING_SNAKE_CASE							=			key






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







			def __getitem__(							self		,       a)   ->							Union[str, Any]:
						return self.dataset[i][self.key]



class 		_snake_case	(	A__       ):






			def __init__(							self		,       a		,       a		,       a)   ->							List[Any]:
						SCREAMING_SNAKE_CASE							=			dataset
						SCREAMING_SNAKE_CASE							=			keya
						SCREAMING_SNAKE_CASE							=			keya






			def __len__(							self)   ->							Dict:
						return len(self.dataset)







			def __getitem__(							self		,       a)   ->							Optional[Any]:
						return {"text": self.dataset[i][self.keya], "text_pair": self.dataset[i][self.keya]}










from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			None:





				"""simple docstring"""


				__lowerCamelCase			,		__lowerCamelCase									=       analyze_text(UpperCamelCase__ )
				__lowerCamelCase									=       list(' ' + ascii_lowercase )
				# what is our total sum of probabilities.
				__lowerCamelCase									=       sum(single_char_strings.values() )

				# one length string
				__lowerCamelCase									=       0
				# for each alpha we go in our dict and if it is in it we calculate entropy
				for ch in my_alphas:
								if ch in single_char_strings:
												__lowerCamelCase									=       single_char_strings[ch]
												__lowerCamelCase									=       my_str / all_sum
												my_fir_sum += prob * math.loga(UpperCamelCase__ )  # entropy formula.

    # print entropy
				print(F"""{round(-1 * my_fir_sum ):.1f}""" )

				# two len string
				__lowerCamelCase									=       sum(two_char_strings.values() )
				__lowerCamelCase									=       0
				# for each alpha (two in size) calculate entropy.
				for cha in my_alphas:
								for cha in my_alphas:
												__lowerCamelCase									=       cha + cha
												if sequence in two_char_strings:
																__lowerCamelCase									=       two_char_strings[sequence]
																__lowerCamelCase									=       int(UpperCamelCase__ ) / all_sum
																my_sec_sum += prob * math.loga(UpperCamelCase__ )

    # print second entropy
				print(F"""{round(-1 * my_sec_sum ):.1f}""" )

				# print the difference between them
				print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )







def      lowerCamelCase_						(			UpperCamelCase__							: str )	->			tuple[dict, dict]:





				"""simple docstring"""


				__lowerCamelCase									=       Counter()  # type: ignore
				__lowerCamelCase									=       Counter()  # type: ignore
				single_char_strings[text[-1]] += 1

				# first case when we have space at start.
				two_char_strings[" " + text[0]] += 1
				for i in range(0					,	len(UpperCamelCase__ ) - 1 ):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings







def      lowerCamelCase_						(			)	->			Dict:





				"""simple docstring"""


				import doctest

				doctest.testmod()
				# text = (
				#     "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
				#     "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
				#     "jointure saw horrible. He private he on be imagine suppose. Fertile "
				#     "beloved evident through no service elderly is. Blind there if every no so "
				#     "at. Own neglected you preferred way sincerity delivered his attempted. To "
				#     "of message cottage windows do besides against uncivil.  Delightful "
				#     "unreserved impossible few estimating men favourable see entreaties. She "
				#     "propriety immediate was improving. He or entrance humoured likewise "
				#     "moderate. Much nor game son say feel. Fat make met can must form into "
				#     "gate. Me we offending prevailed discovery. "
				# )

				# calculate_prob(text)


if __name__ == "__main__":
					main()


