



import numpy as np

from transformers import Pipeline






def        __lowerCAmelCase		(	__lowerCamelCase						:     Union[str, Any]						)	->    Optional[int]:
					__lowerCAmelCase						 =np.max(__lowerCamelCase					, axis=-1					, keepdims=__lowerCamelCase						)
					__lowerCAmelCase						 =np.exp(outputs - maxes						)
					return shifted_exp / shifted_exp.sum(axis=-1					, keepdims=__lowerCamelCase						)






class     __a			(  SCREAMING_SNAKE_CASE					):



	def 		UpperCamelCase				(    self				:     Any					,						**snake_case_				:     List[Any])->				Dict:
						__lowerCAmelCase						 ={}
						if "second_text" in kwargs:
											__lowerCAmelCase						 =kwargs["""second_text"""]
						return preprocess_kwargs, {}, {}



	def 		UpperCamelCase				(    self				:     Any					,						snake_case_				:     List[Any]					,						snake_case_				:     List[Any]=None)->				List[Any]:
						return self.tokenizer(snake_case_					,						text_pair=snake_case_					,						return_tensors=self.framework)



	def 		UpperCamelCase				(    self				:     List[str]					,						snake_case_				:     Any)->				Dict:
						return self.model(**snake_case_)



	def 		UpperCamelCase				(    self				:     List[Any]					,						snake_case_				:     Tuple)->				Union[str, Any]:
						__lowerCAmelCase						 =model_outputs.logits[0].numpy()
						__lowerCAmelCase						 =softmax(snake_case_)

						__lowerCAmelCase						 =np.argmax(snake_case_)
						__lowerCAmelCase						 =self.model.config.idalabel[best_class]
						__lowerCAmelCase						 =probabilities[best_class].item()
						__lowerCAmelCase						 =logits.tolist()
						return {"label": label, "score": score, "logits": logits}









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    None:
					__lowerCAmelCase ,						__lowerCAmelCase						 =analyze_text(__lowerCamelCase						)
					__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(__lowerCamelCase						)  # 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(__lowerCamelCase						) / all_sum
																				my_sec_sum += prob * math.loga(__lowerCamelCase						)

    # 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		(	__lowerCamelCase						:     str						)	->    tuple[dict, dict]:
					__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(__lowerCamelCase						) - 1						):
										single_char_strings[text[i]] += 1
										two_char_strings[text[i : i + 2]] += 1
					return single_char_strings, two_char_strings






def        __lowerCAmelCase		(	)	->    str:
					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()




