
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors




@require_tokenizers
class 				lowerCamelCase__							(       __lowercase  ,		unittest.TestCase):




	'''simple docstring'''




	_A			=  MvpTokenizer
	_A			=  MvpTokenizerFast
	_A			=  True
	_A			=  filter_roberta_detectors







	def      _lowerCamelCase       (      self	:List[str]		)	->				Tuple:
								super().setUp()
								__UpperCamelCase			:     Optional[Any]         =      [
								    "l",
								    "o",
								    "w",
								    "e",
								    "r",
								    "s",
								    "t",
								    "i",
								    "d",
								    "n",
								    "\u0120",
								    "\u0120l",
								    "\u0120n",
								    "\u0120lo",
								    "\u0120low",
								    "er",
								    "\u0120lowest",
								    "\u0120newer",
								    "\u0120wider",
								    "<unk>",
								]
								__UpperCamelCase			:     Dict         =      dict(zip(a  ,     range(len(a		)		)		)		)
								__UpperCamelCase			:     Any         =      ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
								__UpperCamelCase			:     Dict         =      {"unk_token": "<unk>"}

								__UpperCamelCase			:     Optional[int]         =      os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["vocab_file"]		)
								__UpperCamelCase			:     Union[str, Any]         =      os.path.join(self.tmpdirname  ,     VOCAB_FILES_NAMES["merges_file"]		)
								with open(self.vocab_file  ,     "w"  ,     encoding="utf-8"		) as fp:
															fp.write(json.dumps(a		) + "\n"		)
								with open(self.merges_file  ,     "w"  ,     encoding="utf-8"		) as fp:
															fp.write("\n".join(a		)		)







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     **a	:str		)	->				Any:
								kwargs.update(self.special_tokens_map		)
								return self.tokenizer_class.from_pretrained(self.tmpdirname  ,     **a		)







	def      _lowerCamelCase       (      self	:List[str]  ,     **a	:Dict		)	->				int:
								kwargs.update(self.special_tokens_map		)
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname  ,     **a		)







	def      _lowerCamelCase       (      self	:Dict  ,     a	:List[Any]		)	->				str:
								return "lower newer", "lower newer"







	@cached_property
	def      _lowerCamelCase       (      self	:Any		)	->				Union[str, Any]:
								return MvpTokenizer.from_pretrained("RUCAIBox/mvp"		)







	@cached_property
	def      _lowerCamelCase       (      self	:int		)	->				Optional[Any]:
								return MvpTokenizerFast.from_pretrained("RUCAIBox/mvp"		)







	@require_torch
	def      _lowerCamelCase       (      self	:Union[str, Any]		)	->				Union[str, Any]:
								__UpperCamelCase			:     List[str]         =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
								__UpperCamelCase			:     Optional[int]         =      [0, 2_5_0, 2_5_1, 1_7_8_1_8, 1_3, 3_9_1_8_6, 1_9_3_8, 4, 2]

								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															__UpperCamelCase			:     Dict         =      tokenizer(a  ,     max_length=len(a		)  ,     padding=a  ,     return_tensors="pt"		)
															self.assertIsInstance(a  ,     a		)

															self.assertEqual((2, 9)  ,     batch.input_ids.shape		)
															self.assertEqual((2, 9)  ,     batch.attention_mask.shape		)
															__UpperCamelCase			:     str         =      batch.input_ids.tolist()[0]
															self.assertListEqual(a  ,     a		)
															# Test that special tokens are reset







	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				Optional[Any]:
								__UpperCamelCase			:     List[Any]         =      ["A long paragraph for summarization.", "Another paragraph for summarization."]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															__UpperCamelCase			:     Optional[Any]         =      tokenizer(a  ,     padding=a  ,     return_tensors="pt"		)
															# check if input_ids are returned and no labels
															self.assertIn("input_ids"  ,     a		)
															self.assertIn("attention_mask"  ,     a		)
															self.assertNotIn("labels"  ,     a		)
															self.assertNotIn("decoder_attention_mask"  ,     a		)







	@require_torch
	def      _lowerCamelCase       (      self	:Any		)	->				Optional[Any]:
								__UpperCamelCase			:     List[str]         =      [
								    "Summary of the text.",
								    "Another summary.",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															__UpperCamelCase			:     List[Any]         =      tokenizer(text_target=a  ,     max_length=3_2  ,     padding="max_length"  ,     return_tensors="pt"		)
															self.assertEqual(3_2  ,     targets["input_ids"].shape[1]		)







	@require_torch
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				List[str]:
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															__UpperCamelCase			:     int         =      tokenizer(
															    ["I am a small frog" * 1_0_2_4, "I am a small frog"]  ,     padding=a  ,     truncation=a  ,     return_tensors="pt"		)
															self.assertIsInstance(a  ,     a		)
															self.assertEqual(batch.input_ids.shape  ,     (2, 1_0_2_4)		)







	@require_torch
	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Optional[int]:
								__UpperCamelCase			:     Any         =      ["A long paragraph for summarization."]
								__UpperCamelCase			:     Any         =      [
								    "Summary of the text.",
								]
								for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
															__UpperCamelCase			:     str         =      tokenizer(a  ,     text_target=a  ,     return_tensors="pt"		)
															__UpperCamelCase			:     Tuple         =      inputs["input_ids"]
															__UpperCamelCase			:     Optional[int]         =      inputs["labels"]
															self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()		)
															self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()		)
															self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()		)
															self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()		)







	def      _lowerCamelCase       (      self	:Optional[int]		)	->				Dict:
								pass







	def      _lowerCamelCase       (      self	:str		)	->				Optional[int]:
								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
															with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})'		):
																						__UpperCamelCase			:     Dict         =      self.rust_tokenizer_class.from_pretrained(a  ,     **a		)
																						__UpperCamelCase			:     Any         =      self.tokenizer_class.from_pretrained(a  ,     **a		)
																						__UpperCamelCase			:     List[Any]         =      "A, <mask> AllenNLP sentence."
																						__UpperCamelCase			:     str         =      tokenizer_r.encode_plus(a  ,     add_special_tokens=a  ,     return_token_type_ids=a		)
																						__UpperCamelCase			:     Tuple         =      tokenizer_p.encode_plus(a  ,     add_special_tokens=a  ,     return_token_type_ids=a		)

																						# token_type_ids should put 0 everywhere
																						self.assertEqual(sum(tokens_r["token_type_ids"]		)  ,     sum(tokens_p["token_type_ids"]		)		)

																						# attention_mask should put 1 everywhere, so sum over length should be 1
																						self.assertEqual(
																						    sum(tokens_r["attention_mask"]		) / len(tokens_r["attention_mask"]		)  ,     sum(tokens_p["attention_mask"]		) / len(tokens_p["attention_mask"]		)  ,     )

																						__UpperCamelCase			:     Optional[Any]         =      tokenizer_r.convert_ids_to_tokens(tokens_r["input_ids"]		)
																						__UpperCamelCase			:     List[Any]         =      tokenizer_p.convert_ids_to_tokens(tokens_p["input_ids"]		)

																						# Rust correctly handles the space before the mask while python doesnt
																						self.assertSequenceEqual(tokens_p["input_ids"]  ,     [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)
																						self.assertSequenceEqual(tokens_r["input_ids"]  ,     [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2]		)

																						self.assertSequenceEqual(
																						    a  ,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)
																						self.assertSequenceEqual(
																						    a  ,     ["<s>", "A", ",", "<mask>", "ĠAllen", "N", "LP", "Ġsentence", ".", "</s>"]		)



from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					None:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     Optional[Any]         =      analyze_text(_lowerCamelCase)
							__UpperCamelCase			:     List[str]         =      list(" " + ascii_lowercase)
							# what is our total sum of probabilities.
							__UpperCamelCase			:     Any         =      sum(single_char_strings.values())

							# one length string
							__UpperCamelCase			:     Optional[Any]         =      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			:     List[Any]         =      single_char_strings[ch]
																					__UpperCamelCase			:     List[str]         =      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
							__UpperCamelCase			:     Optional[Any]         =      sum(two_char_strings.values())
							__UpperCamelCase			:     Tuple         =      0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					__UpperCamelCase			:     List[str]         =      cha + cha
																					if sequence in two_char_strings:
																												__UpperCamelCase			:     Optional[Any]         =      two_char_strings[sequence]
																												__UpperCamelCase			:     Any         =      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 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					tuple[dict, dict]:






							'''simple docstring'''





							__UpperCamelCase			:     Tuple         =      Counter()  # type: ignore
							__UpperCamelCase			:     Any         =      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 			_SCREAMING_SNAKE_CASE      (	)      ->					List[str]:






							'''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()