
import warnings
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				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					["image_processor", "tokenizer"]
	snake_case_    =					"FlavaImageProcessor"
	snake_case_    =					("BertTokenizer", "BertTokenizerFast")






	def __init__(	self :			Any							,A :			Any=None							,A :			List[Any]=None							,**A :			List[str]			):
					__A             =			None
					if "feature_extractor" in kwargs:
									warnings.warn(
									    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
									    " instead."							,A							,)
									__A             =			kwargs.pop("feature_extractor"			)

					__A             =			image_processor if image_processor is not None else feature_extractor
					if image_processor is None:
									raise ValueError("You need to specify an `image_processor`."			)
					if tokenizer is None:
									raise ValueError("You need to specify a `tokenizer`."			)

					super().__init__(A							,A			)
					__A             =			self.image_processor






	def __call__(	self :			Any							,A :			Optional[ImageInput] = None							,A :			Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None							,A :			bool = True							,A :			Union[bool, str, PaddingStrategy] = False							,A :			Union[bool, str, TruncationStrategy] = False							,A :			Optional[int] = None							,A :			int = 0							,A :			Optional[int] = None							,A :			Optional[bool] = None							,A :			Optional[bool] = None							,A :			Optional[bool] = None							,A :			Optional[bool] = None							,A :			bool = False							,A :			bool = False							,A :			bool = False							,A :			bool = False							,A :			bool = True							,A :			Optional[Union[str, TensorType]] = None							,**A :			Union[str, Any]							,):

					if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none."			)

					if text is not None:
									__A             =			self.tokenizer(
									    text=A							,add_special_tokens=A							,padding=A							,truncation=A							,max_length=A							,stride=A							,pad_to_multiple_of=A							,return_token_type_ids=A							,return_attention_mask=A							,return_overflowing_tokens=A							,return_special_tokens_mask=A							,return_offsets_mapping=A							,return_length=A							,verbose=A							,return_tensors=A							,**A							,)
					if images is not None:
									__A             =			self.image_processor(
									    A							,return_image_mask=A							,return_codebook_pixels=A							,return_tensors=A							,**A							,)

					if text is not None and images is not None:
									encoding.update(A			)
									return encoding
					elif text is not None:
									return encoding
					else:
									return BatchEncoding(data=dict(**A			)							,tensor_type=A			)






	def     UpperCamelCase_  (	self :			Tuple							,*A :			str							,**A :			Optional[Any]			):
					return self.tokenizer.batch_decode(*A							,**A			)






	def     UpperCamelCase_  (	self :			Union[str, Any]							,*A :			Optional[int]							,**A :			Union[str, Any]			):
					return self.tokenizer.decode(*A							,**A			)






	@property
	def     UpperCamelCase_  (	self :			Tuple			):
					__A             =			self.tokenizer.model_input_names
					__A             =			self.image_processor.model_input_names
					return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)






	@property
	def     UpperCamelCase_  (	self :			str			):
					warnings.warn(
					    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,A							,)
					return self.image_processor_class







	@property
	def     UpperCamelCase_  (	self :			List[Any]			):
					warnings.warn(
					    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,A							,)
					return self.image_processor


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()
