
from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase
def  lowerCAmelCase__			(  SCREAMING_SNAKE_CASE_     )				->		None:
			lowerCAmelCase__      ,				lowerCAmelCase__      :      List[Any]         =		analyze_text(SCREAMING_SNAKE_CASE_     )
			lowerCAmelCase__      :      List[Any]         =		list(' ' + ascii_lowercase     )
			# what is our total sum of probabilities.
			lowerCAmelCase__      :      Tuple         =		sum(single_char_strings.values()     )

			# one length string
			lowerCAmelCase__      :      Optional[int]         =		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:
									lowerCAmelCase__      :      Optional[int]         =		single_char_strings[ch]
									lowerCAmelCase__      :      Dict         =		my_str / all_sum
									my_fir_sum += prob * math.loga(SCREAMING_SNAKE_CASE_     )  # entropy formula.

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

			# two len string
			lowerCAmelCase__      :      Tuple         =		sum(two_char_strings.values()     )
			lowerCAmelCase__      :      Union[str, Any]         =		0
			# for each alpha (two in size) calculate entropy.
			for cha in my_alphas:
						for cha in my_alphas:
									lowerCAmelCase__      :      Union[str, Any]         =		cha + cha
									if sequence in two_char_strings:
												lowerCAmelCase__      :      Optional[Any]         =		two_char_strings[sequence]
												lowerCAmelCase__      :      Optional[int]         =		int(SCREAMING_SNAKE_CASE_     ) / all_sum
												my_sec_sum += prob * math.loga(SCREAMING_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__			(  SCREAMING_SNAKE_CASE_     )				->		tuple[dict, dict]:
			lowerCAmelCase__      :      int         =		Counter()  # type: ignore
			lowerCAmelCase__      :      Optional[int]         =		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(SCREAMING_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__			(  )				->		Optional[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()

from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_tf_available():
					import tensorflow as tf

					from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					from ..tf_utils import stable_softmax

if is_torch_available():
					from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCamelCase__          =		logging.get_logger(__name__)







@add_end_docstrings(__magic_name__	)
class      A__							(     __magic_name__	):






				def __init__(						self					:      int    ,     *a					:      Dict    ,     **a					:      Union[str, Any]   ):






							'''simple docstring'''







							super().__init__(*a    ,     **a   )
							requires_backends(self    ,     'vision'   )
							self.check_model_type(
							    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
							    if self.framework == 'tf'
							    else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING   )






				def 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[str]=None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{}
							if top_k is not None:
										lowerCAmelCase__      :      Tuple         =		top_k
							return {}, {}, postprocess_params






				def __call__(						self					:      Any    ,     a					:      Union[str, List[str], "Image.Image", List["Image.Image"]]    ,     **a					:      List[Any]   ):






							'''simple docstring'''







							return super().__call__(a    ,     **a   )






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		load_image(a   )
							lowerCAmelCase__      :      Optional[int]         =		self.image_processor(images=a    ,     return_tensors=self.framework   )
							return model_inputs






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.model(**a   )
							return model_outputs






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      List[Any]    ,     a					:      List[Any]=5   ):






							'''simple docstring'''







							if top_k > self.model.config.num_labels:
										lowerCAmelCase__      :      Optional[int]         =		self.model.config.num_labels

							if self.framework == "pt":
										lowerCAmelCase__      :      List[Any]         =		model_outputs.logits.softmax(-1   )[0]
										lowerCAmelCase__      ,				lowerCAmelCase__      :      Optional[int]         =		probs.topk(a   )
							elif self.framework == "tf":
										lowerCAmelCase__      :      Any         =		stable_softmax(model_outputs.logits    ,     axis=-1   )[0]
										lowerCAmelCase__      :      Any         =		tf.math.top_k(a    ,     k=a   )
										lowerCAmelCase__      ,				lowerCAmelCase__      :      int         =		topk.values.numpy(), topk.indices.numpy()
							else:
										raise ValueError(f'''Unsupported framework: {self.framework}'''   )

							lowerCAmelCase__      :      List[Any]         =		scores.tolist()
							lowerCAmelCase__      :      List[str]         =		ids.tolist()
							return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(a    ,     a   )]