


"""simple docstring"""
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase







def   _a			( _SCREAMING_SNAKE_CASE			)      ->			None:
							snake_case_    ,  snake_case_          =						analyze_text(lowercase_			)
							snake_case_          =						list(""" """ + ascii_lowercase			)
							# what is our total sum of probabilities.
							snake_case_          =						sum(single_char_strings.values()			)

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

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

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

    # 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   _a			( _SCREAMING_SNAKE_CASE			)      ->			tuple[dict, dict]:
							snake_case_          =						Counter()  # type: ignore
							snake_case_          =						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(lowercase_			) - 1			):
														single_char_strings[text[i]] += 1
														two_char_strings[text[i : i + 2]] += 1
							return single_char_strings, two_char_strings







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







"""simple docstring"""
import shutil
import tempfile
import unittest

from transformers import ClapFeatureExtractor, ClapProcessor, RobertaTokenizer, RobertaTokenizerFast
from transformers.testing_utils import require_sentencepiece, require_torchaudio

from .test_feature_extraction_clap import floats_list





@require_torchaudio
@require_sentencepiece
class        __A       (unittest.TestCase):





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: Dict    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						"""laion/clap-htsat-unfused"""
														snake_case_          =						tempfile.mkdtemp()



							def 						lowerCAmelCase    (			self							: List[str]						,    **UpperCAmelCase_							: Tuple    )   ->str:



														"""simple docstring"""



														return RobertaTokenizer.from_pretrained(self.checkpoint						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: Optional[Any]						,    **UpperCAmelCase_							: Any    )   ->Optional[Any]:



														"""simple docstring"""



														return ClapFeatureExtractor.from_pretrained(self.checkpoint						,    **UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: str    )   ->Any:



														"""simple docstring"""



														shutil.rmtree(self.tmpdirname    )



							def 						lowerCAmelCase    (			self							: Tuple    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.get_tokenizer()
														snake_case_          =						self.get_feature_extractor()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														processor.save_pretrained(self.tmpdirname    )
														snake_case_          =						ClapProcessor.from_pretrained(self.tmpdirname    )

														self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer.get_vocab()    )
														self.assertIsInstance(processor.tokenizer						,    UpperCAmelCase_    )

														self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor.to_json_string()    )
														self.assertIsInstance(processor.feature_extractor						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: int    )   ->List[str]:



														"""simple docstring"""



														snake_case_          =						ClapProcessor(tokenizer=self.get_tokenizer()						,    feature_extractor=self.get_feature_extractor()    )
														processor.save_pretrained(self.tmpdirname    )

														snake_case_          =						self.get_tokenizer(bos_token="""(BOS)"""						,    eos_token="""(EOS)"""    )
														snake_case_          =						self.get_feature_extractor(do_normalize=UpperCAmelCase_						,    padding_value=1.0    )

														snake_case_          =						ClapProcessor.from_pretrained(
														    self.tmpdirname						,    bos_token="""(BOS)"""						,    eos_token="""(EOS)"""						,    do_normalize=UpperCAmelCase_						,    padding_value=1.0    )

														self.assertEqual(processor.tokenizer.get_vocab()						,    tokenizer_add_kwargs.get_vocab()    )
														self.assertIsInstance(processor.tokenizer						,    UpperCAmelCase_    )

														self.assertEqual(processor.feature_extractor.to_json_string()						,    feature_extractor_add_kwargs.to_json_string()    )
														self.assertIsInstance(processor.feature_extractor						,    UpperCAmelCase_    )



							def 						lowerCAmelCase    (			self							: str    )   ->Dict:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						floats_list((3, 1_000)    )

														snake_case_          =						feature_extractor(UpperCAmelCase_						,    return_tensors="""np"""    )
														snake_case_          =						processor(audios=UpperCAmelCase_						,    return_tensors="""np"""    )

														for key in input_feat_extract.keys():
																					self.assertAlmostEqual(input_feat_extract[key].sum()						,    input_processor[key].sum()						,    delta=1E-2    )



							def 						lowerCAmelCase    (			self							: Union[str, Any]    )   ->Optional[int]:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						"""This is a test string"""

														snake_case_          =						processor(text=UpperCAmelCase_    )

														snake_case_          =						tokenizer(UpperCAmelCase_    )

														for key in encoded_tok.keys():
																					self.assertListEqual(encoded_tok[key]						,    encoded_processor[key]    )



							def 						lowerCAmelCase    (			self							: Optional[int]    )   ->Tuple:



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														snake_case_          =						[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

														snake_case_          =						processor.batch_decode(UpperCAmelCase_    )
														snake_case_          =						tokenizer.batch_decode(UpperCAmelCase_    )

														self.assertListEqual(UpperCAmelCase_						,    UpperCAmelCase_    )



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



														"""simple docstring"""



														snake_case_          =						self.get_feature_extractor()
														snake_case_          =						self.get_tokenizer()

														snake_case_          =						ClapProcessor(tokenizer=UpperCAmelCase_						,    feature_extractor=UpperCAmelCase_    )

														self.assertListEqual(
														    processor.model_input_names[2:]						,    feature_extractor.model_input_names						,    msg="""`processor` and `feature_extractor` model input names do not match"""						,    )



