
"""simple docstring"""





from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType






class    _UpperCAmelCase(   lowerCamelCase     ):
		lowercase__      =				['image_processor', 'tokenizer']
		lowercase__      =				'BlipImageProcessor'
		lowercase__      =				'AutoTokenizer'



		def __init__( self    ,		__a    ,		__a)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  False
							super().__init__(__a    ,		__a)
							_UpperCamelCase										=  self.image_processor



		def __call__( self    ,		__a = None    ,		__a = None    ,		__a = True    ,		__a = False    ,		__a = None    ,		__a = None    ,		__a = 0    ,		__a = None    ,		__a = None    ,		__a = False    ,		__a = False    ,		__a = False    ,		__a = False    ,		__a = False    ,		__a = True    ,		__a = None    ,		**__a    ,		)	->	BatchEncoding:






							'''simple docstring'''
							if images is None and text is None:
												raise ValueError('''You have to specify either images or text.''')

							# Get only text
							if images is None:
												_UpperCamelCase										=  self.tokenizer
												_UpperCamelCase										=  self.tokenizer(
												    text=__a    ,		add_special_tokens=__a    ,		padding=__a    ,		truncation=__a    ,		max_length=__a    ,		stride=__a    ,		pad_to_multiple_of=__a    ,		return_attention_mask=__a    ,		return_overflowing_tokens=__a    ,		return_special_tokens_mask=__a    ,		return_offsets_mapping=__a    ,		return_token_type_ids=__a    ,		return_length=__a    ,		verbose=__a    ,		return_tensors=__a    ,		**__a    ,		)
												return text_encoding

							# add pixel_values
							_UpperCamelCase										=  self.image_processor(__a    ,		return_tensors=__a)

							if text is not None:
												_UpperCamelCase										=  self.tokenizer(
												    text=__a    ,		add_special_tokens=__a    ,		padding=__a    ,		truncation=__a    ,		max_length=__a    ,		stride=__a    ,		pad_to_multiple_of=__a    ,		return_attention_mask=__a    ,		return_overflowing_tokens=__a    ,		return_special_tokens_mask=__a    ,		return_offsets_mapping=__a    ,		return_token_type_ids=__a    ,		return_length=__a    ,		verbose=__a    ,		return_tensors=__a    ,		**__a    ,		)
							else:
												_UpperCamelCase										=  None

							if text_encoding is not None:
												encoding_image_processor.update(__a)

							return encoding_image_processor



		def 						UpperCAmelCase							( self    ,		*__a    ,		**__a)	->	Optional[Any]:






							'''simple docstring'''
							return self.tokenizer.batch_decode(*__a    ,		**__a)



		def 						UpperCAmelCase							( self    ,		*__a    ,		**__a)	->	List[Any]:






							'''simple docstring'''
							return self.tokenizer.decode(*__a    ,		**__a)




		@property
		# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
		def 						UpperCAmelCase							( self)	->	Any:






							'''simple docstring'''
							_UpperCamelCase										=  self.tokenizer.model_input_names
							_UpperCamelCase										=  self.image_processor.model_input_names
							return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))








"""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()






