
"""simple docstring"""




import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
					from transformers.models.rag.configuration_rag import RagConfig
					from transformers.models.rag.tokenization_rag import RagTokenizer





@require_faiss
@require_torch
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def     UpperCamelCase_  (	self :			Optional[int]			):
					__A             =			tempfile.mkdtemp()
					__A             =			8

					# DPR tok
					__A             =			[
					    "[UNK]",
					    "[CLS]",
					    "[SEP]",
					    "[PAD]",
					    "[MASK]",
					    "want",
					    "##want",
					    "##ed",
					    "wa",
					    "un",
					    "runn",
					    "##ing",
					    ",",
					    "low",
					    "lowest",
					]
					__A             =			os.path.join(self.tmpdirname							,"dpr_tokenizer"			)
					os.makedirs(_snake_case							,exist_ok=_snake_case			)
					__A             =			os.path.join(_snake_case							,DPR_VOCAB_FILES_NAMES["vocab_file"]			)
					with open(self.vocab_file							,"w"							,encoding="utf-8"			) as vocab_writer:
									vocab_writer.write("".join([x + "\n" for x in vocab_tokens]			)			)

					# BART tok
					__A             =			[
					    "l",
					    "o",
					    "w",
					    "e",
					    "r",
					    "s",
					    "t",
					    "i",
					    "d",
					    "n",
					    "\u0120",
					    "\u0120l",
					    "\u0120n",
					    "\u0120lo",
					    "\u0120low",
					    "er",
					    "\u0120lowest",
					    "\u0120newer",
					    "\u0120wider",
					    "<unk>",
					]
					__A             =			dict(zip(_snake_case							,range(len(_snake_case			)			)			)			)
					__A             =			["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
					__A             =			{"unk_token": "<unk>"}

					__A             =			os.path.join(self.tmpdirname							,"bart_tokenizer"			)
					os.makedirs(_snake_case							,exist_ok=_snake_case			)
					__A             =			os.path.join(_snake_case							,BART_VOCAB_FILES_NAMES["vocab_file"]			)
					__A             =			os.path.join(_snake_case							,BART_VOCAB_FILES_NAMES["merges_file"]			)
					with open(self.vocab_file							,"w"							,encoding="utf-8"			) as fp:
									fp.write(json.dumps(_snake_case			) + "\n"			)
					with open(self.merges_file							,"w"							,encoding="utf-8"			) as fp:
									fp.write("\n".join(_snake_case			)			)






	def     UpperCamelCase_  (	self :			Tuple			):
					return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,"dpr_tokenizer"			)			)






	def     UpperCamelCase_  (	self :			Optional[Any]			):
					return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname							,"bart_tokenizer"			)			)






	def     UpperCamelCase_  (	self :			Union[str, Any]			):
					shutil.rmtree(self.tmpdirname			)






	@require_tokenizers
	def     UpperCamelCase_  (	self :			Dict			):
					__A             =			os.path.join(self.tmpdirname							,"rag_tokenizer"			)
					__A             =			RagConfig(question_encoder=DPRConfig().to_dict()							,generator=BartConfig().to_dict()			)
					__A             =			RagTokenizer(question_encoder=self.get_dpr_tokenizer()							,generator=self.get_bart_tokenizer()			)
					rag_config.save_pretrained(_snake_case			)
					rag_tokenizer.save_pretrained(_snake_case			)
					__A             =			RagTokenizer.from_pretrained(_snake_case							,config=_snake_case			)
					self.assertIsInstance(new_rag_tokenizer.question_encoder							,_snake_case			)
					self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()							,rag_tokenizer.question_encoder.get_vocab()			)
					self.assertIsInstance(new_rag_tokenizer.generator							,_snake_case			)
					self.assertEqual(new_rag_tokenizer.generator.get_vocab()							,rag_tokenizer.generator.get_vocab()			)






	@slow
	def     UpperCamelCase_  (	self :			str			):
					__A             =			RagTokenizer.from_pretrained("facebook/rag-token-nq"			)
					__A             =			[
					    "who got the first nobel prize in physics",
					    "when is the next deadpool movie being released",
					    "which mode is used for short wave broadcast service",
					    "who is the owner of reading football club",
					    "when is the next scandal episode coming out",
					    "when is the last time the philadelphia won the superbowl",
					    "what is the most current adobe flash player version",
					    "how many episodes are there in dragon ball z",
					    "what is the first step in the evolution of the eye",
					    "where is gall bladder situated in human body",
					    "what is the main mineral in lithium batteries",
					    "who is the president of usa right now",
					    "where do the greasers live in the outsiders",
					    "panda is a national animal of which country",
					    "what is the name of manchester united stadium",
					]
					__A             =			tokenizer(_snake_case			)
					self.assertIsNotNone(_snake_case			)







	@slow
	def     UpperCamelCase_  (	self :			Optional[Any]			):
					__A             =			RagTokenizer.from_pretrained("facebook/rag-sequence-nq"			)
					__A             =			[
					    "who got the first nobel prize in physics",
					    "when is the next deadpool movie being released",
					    "which mode is used for short wave broadcast service",
					    "who is the owner of reading football club",
					    "when is the next scandal episode coming out",
					    "when is the last time the philadelphia won the superbowl",
					    "what is the most current adobe flash player version",
					    "how many episodes are there in dragon ball z",
					    "what is the first step in the evolution of the eye",
					    "where is gall bladder situated in human body",
					    "what is the main mineral in lithium batteries",
					    "who is the president of usa right now",
					    "where do the greasers live in the outsiders",
					    "panda is a national animal of which country",
					    "what is the name of manchester united stadium",
					]
					__A             =			tokenizer(_snake_case			)
					self.assertIsNotNone(_snake_case			)


from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				__A      ,							__A             =			analyze_text(a_						)
				__A             =			list(" " + ascii_lowercase						)
				# what is our total sum of probabilities.
				__A             =			sum(single_char_strings.values()						)

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

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

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

    # 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 						UpperCAmelCase      (     a_						)		->     tuple[dict, dict]:







				"""simple docstring"""



				__A             =			Counter()  # type: ignore
				__A             =			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(a_						) - 1						):
								single_char_strings[text[i]] += 1
								two_char_strings[text[i : i + 2]] += 1
				return single_char_strings, two_char_strings


def 						UpperCAmelCase      (     )		->     Optional[Any]:







				"""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()
