
"""simple docstring"""





import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


_a														=		logging.getLogger(__name__)






@dataclass
class    _UpperCAmelCase:
		lowercase__      =				42
		lowercase__      =				42
		lowercase__      =				42






@dataclass
class    _UpperCAmelCase:
		lowercase__      =				42
		lowercase__      =				42
		lowercase__      =				None
		lowercase__      =				None






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				'train'
		lowercase__      =				'dev'
		lowercase__      =				'test'






class    _UpperCAmelCase:



		@staticmethod
		def 						UpperCAmelCase							( __a    ,		__a)	->	List[InputExample]:






							'''simple docstring'''
							raise NotImplementedError



		@staticmethod
		def 						UpperCAmelCase							( __a)	->	List[str]:






							'''simple docstring'''
							raise NotImplementedError




		@staticmethod
		def 						UpperCAmelCase							( __a    ,		__a    ,		__a    ,		__a    ,		__a=False    ,		__a="[CLS]"    ,		__a=1    ,		__a="[SEP]"    ,		__a=False    ,		__a=False    ,		__a=0    ,		__a=0    ,		__a=-1_00    ,		__a=0    ,		__a=True    ,		)	->	List[InputFeatures]:






							'''simple docstring'''
							_UpperCamelCase										=  {label: i for i, label in enumerate(__a)}

							_UpperCamelCase										=  []
							for ex_index, example in enumerate(__a):
												if ex_index % 1_00_00 == 0:
																	logger.info('''Writing example %d of %d'''    ,		__a    ,		len(__a))

												_UpperCamelCase										=  []
												_UpperCamelCase										=  []
												for word, label in zip(example.words    ,		example.labels):
																	_UpperCamelCase										=  tokenizer.tokenize(__a)

																	# bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
																	if len(__a) > 0:
																						tokens.extend(__a)
																						# Use the real label id for the first token of the word, and padding ids for the remaining tokens
																						label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(__a) - 1))

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
												_UpperCamelCase										=  tokenizer.num_special_tokens_to_add()
												if len(__a) > max_seq_length - special_tokens_count:
																	_UpperCamelCase										=  tokens[: (max_seq_length - special_tokens_count)]
																	_UpperCamelCase										=  label_ids[: (max_seq_length - special_tokens_count)]

												# The convention in BERT is:
												# (a) For sequence pairs:
												#  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
												#  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
												# (b) For single sequences:
												#  tokens:   [CLS] the dog is hairy . [SEP]
												#  type_ids:   0   0   0   0  0     0   0
												#
												# Where "type_ids" are used to indicate whether this is the first
												# sequence or the second sequence. The embedding vectors for `type=0` and
												# `type=1` were learned during pre-training and are added to the wordpiece
												# embedding vector (and position vector). This is not *strictly* necessary
												# since the [SEP] token unambiguously separates the sequences, but it makes
												# it easier for the model to learn the concept of sequences.
												#
												# For classification tasks, the first vector (corresponding to [CLS]) is
												# used as the "sentence vector". Note that this only makes sense because
												# the entire model is fine-tuned.
												tokens += [sep_token]
												label_ids += [pad_token_label_id]
												if sep_token_extra:
																	# roberta uses an extra separator b/w pairs of sentences
																	tokens += [sep_token]
																	label_ids += [pad_token_label_id]
												_UpperCamelCase										=  [sequence_a_segment_id] * len(__a)

												if cls_token_at_end:
																	tokens += [cls_token]
																	label_ids += [pad_token_label_id]
																	segment_ids += [cls_token_segment_id]
												else:
																	_UpperCamelCase										=  [cls_token] + tokens
																	_UpperCamelCase										=  [pad_token_label_id] + label_ids
																	_UpperCamelCase										=  [cls_token_segment_id] + segment_ids

												_UpperCamelCase										=  tokenizer.convert_tokens_to_ids(__a)

												# The mask has 1 for real tokens and 0 for padding tokens. Only real
												# tokens are attended to.
												_UpperCamelCase										=  [1 if mask_padding_with_zero else 0] * len(__a)

												# Zero-pad up to the sequence length.
												_UpperCamelCase										=  max_seq_length - len(__a)
												if pad_on_left:
																	_UpperCamelCase										=  ([pad_token] * padding_length) + input_ids
																	_UpperCamelCase										=  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
																	_UpperCamelCase										=  ([pad_token_segment_id] * padding_length) + segment_ids
																	_UpperCamelCase										=  ([pad_token_label_id] * padding_length) + label_ids
												else:
																	input_ids += [pad_token] * padding_length
																	input_mask += [0 if mask_padding_with_zero else 1] * padding_length
																	segment_ids += [pad_token_segment_id] * padding_length
																	label_ids += [pad_token_label_id] * padding_length

												assert len(__a) == max_seq_length
												assert len(__a) == max_seq_length
												assert len(__a) == max_seq_length
												assert len(__a) == max_seq_length

												if ex_index < 5:
																	logger.info('''*** Example ***''')
																	logger.info('''guid: %s'''    ,		example.guid)
																	logger.info('''tokens: %s'''    ,		''' '''.join([str(__a) for x in tokens]))
																	logger.info('''input_ids: %s'''    ,		''' '''.join([str(__a) for x in input_ids]))
																	logger.info('''input_mask: %s'''    ,		''' '''.join([str(__a) for x in input_mask]))
																	logger.info('''segment_ids: %s'''    ,		''' '''.join([str(__a) for x in segment_ids]))
																	logger.info('''label_ids: %s'''    ,		''' '''.join([str(__a) for x in label_ids]))

												if "token_type_ids" not in tokenizer.model_input_names:
																	_UpperCamelCase										=  None

												features.append(
												    InputFeatures(
												        input_ids=__a    ,		attention_mask=__a    ,		token_type_ids=__a    ,		label_ids=__a))
							return features


if is_torch_available():
				import torch
				from torch import nn
				from torch.utils.data import Dataset






				class    _UpperCAmelCase(   lowerCamelCase     ):
						lowercase__      =				42
						lowercase__      =				nn.CrossEntropyLoss().ignore_index



						def __init__( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		__a=False    ,		__a = Split.train    ,		)	->	Optional[Any]:






											'''simple docstring'''
											# Load data features from cache or dataset file
											_UpperCamelCase										=  os.path.join(
											    __a    ,		'''cached_{}_{}_{}'''.format(mode.value    ,		tokenizer.__class__.__name__    ,		str(__a))    ,		)

											# Make sure only the first process in distributed training processes the dataset,
											# and the others will use the cache.
											_UpperCamelCase										=  cached_features_file + '''.lock'''
											with FileLock(__a):
																if os.path.exists(__a) and not overwrite_cache:
																					logger.info(F'''Loading features from cached file {cached_features_file}''')
																					_UpperCamelCase										=  torch.load(__a)
																else:
																					logger.info(F'''Creating features from dataset file at {data_dir}''')
																					_UpperCamelCase										=  token_classification_task.read_examples_from_file(__a    ,		__a)
																					# TODO clean up all this to leverage built-in features of tokenizers
																					_UpperCamelCase										=  token_classification_task.convert_examples_to_features(
																					    __a    ,		__a    ,		__a    ,		__a    ,		cls_token_at_end=bool(model_type in ['''xlnet'''])    ,		cls_token=tokenizer.cls_token    ,		cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0    ,		sep_token=tokenizer.sep_token    ,		sep_token_extra=__a    ,		pad_on_left=bool(tokenizer.padding_side == '''left''')    ,		pad_token=tokenizer.pad_token_id    ,		pad_token_segment_id=tokenizer.pad_token_type_id    ,		pad_token_label_id=self.pad_token_label_id    ,		)
																					logger.info(F'''Saving features into cached file {cached_features_file}''')
																					torch.save(self.features    ,		__a)



						def __len__( self)	->	Union[str, Any]:






											'''simple docstring'''
											return len(self.features)




						def __getitem__( self    ,		__a)	->	InputFeatures:






											'''simple docstring'''
											return self.features[i]


if is_tf_available():
				import tensorflow as tf






				class    _UpperCAmelCase:
						lowercase__      =				42
						lowercase__      =				-1_00



						def __init__( self    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a    ,		__a = None    ,		__a=False    ,		__a = Split.train    ,		)	->	str:






											'''simple docstring'''
											_UpperCamelCase										=  token_classification_task.read_examples_from_file(__a    ,		__a)
											# TODO clean up all this to leverage built-in features of tokenizers
											_UpperCamelCase										=  token_classification_task.convert_examples_to_features(
											    __a    ,		__a    ,		__a    ,		__a    ,		cls_token_at_end=bool(model_type in ['''xlnet'''])    ,		cls_token=tokenizer.cls_token    ,		cls_token_segment_id=2 if model_type in ['''xlnet'''] else 0    ,		sep_token=tokenizer.sep_token    ,		sep_token_extra=__a    ,		pad_on_left=bool(tokenizer.padding_side == '''left''')    ,		pad_token=tokenizer.pad_token_id    ,		pad_token_segment_id=tokenizer.pad_token_type_id    ,		pad_token_label_id=self.pad_token_label_id    ,		)

											def gen():
																for ex in self.features:
																					if ex.token_type_ids is None:
																										yield (
																										    {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
																										    ex.label_ids,
																										)
																					else:
																										yield (
																										    {
																										        "input_ids": ex.input_ids,
																										        "attention_mask": ex.attention_mask,
																										        "token_type_ids": ex.token_type_ids,
																										    },
																										    ex.label_ids,
																										)

											if "token_type_ids" not in tokenizer.model_input_names:
																_UpperCamelCase										=  tf.data.Dataset.from_generator(
																    __a    ,		({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa}, tf.intaa)    ,		(
																        {'''input_ids''': tf.TensorShape([None]), '''attention_mask''': tf.TensorShape([None])},
																        tf.TensorShape([None]),
																    )    ,		)
											else:
																_UpperCamelCase										=  tf.data.Dataset.from_generator(
																    __a    ,		({'''input_ids''': tf.intaa, '''attention_mask''': tf.intaa, '''token_type_ids''': tf.intaa}, tf.intaa)    ,		(
																        {
																            '''input_ids''': tf.TensorShape([None]),
																            '''attention_mask''': tf.TensorShape([None]),
																            '''token_type_ids''': tf.TensorShape([None]),
																        },
																        tf.TensorShape([None]),
																    )    ,		)



						def 						UpperCAmelCase							( self)	->	Optional[int]:






											'''simple docstring'''
											_UpperCamelCase										=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features)))

											return self.dataset



						def __len__( self)	->	Any:






											'''simple docstring'''
											return len(self.features)




						def __getitem__( self    ,		__a)	->	InputFeatures:






											'''simple docstring'''
											return self.features[i]








"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 				lowerCamelCase__		(      __snake_case     )							->		None:


					"""simple docstring"""





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

					# one length string
					_UpperCamelCase										=  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:
															_UpperCamelCase										=  single_char_strings[ch]
															_UpperCamelCase										=  my_str / all_sum
															my_fir_sum += prob * math.loga(__snake_case     )  # entropy formula.

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

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

    # 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__		(      __snake_case     )							->		tuple[dict, dict]:


					"""simple docstring"""





					_UpperCamelCase										=  Counter()  # type: ignore
					_UpperCamelCase										=  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(__snake_case     ) - 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()






