






"""simple docstring"""





import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
    from ... import PreTrainedTokenizerBase, TensorType

UpperCamelCase_	:				List[Any]										= logging.get_logger(__name__)


class      _lowercase (     lowerCAmelCase						):
      _a   :     Optional[int]								=   '''vision-encoder-decoder'''
      _a   :     str								=   True


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





            """simple docstring"""







            super().__init__(**a						)
            if "encoder" not in kwargs or "decoder" not in kwargs:
                  raise ValueError(
                      f'''A configuraton of type {self.model_type} cannot be instantiated because '''
                      f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}'''						)

            __snake_case      :    Union[str, Any]  			=kwargs.pop('''encoder'''						)
            __snake_case      :    List[Any]  			=encoder_config.pop('''model_type'''						)
            __snake_case      :    str  			=kwargs.pop('''decoder'''						)
            __snake_case      :    Optional[Any]  			=decoder_config.pop('''model_type'''						)

            __snake_case      :    Optional[int]  			=AutoConfig.for_model(a							,   **a						)
            __snake_case      :    int  			=AutoConfig.for_model(a							,   **a						)
            __snake_case      :    Optional[int]  			=True


      @classmethod
      def _UpperCamelCase						(    cls				:	List[str]							,   a				:	PretrainedConfig							,   a				:	PretrainedConfig							,   **a				:	Dict						):





            """simple docstring"""







            logger.info('''Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config'''						)
            __snake_case      :    str  			=True
            __snake_case      :    List[str]  			=True

            return cls(encoder=encoder_config.to_dict()							,   decoder=decoder_config.to_dict()							,   **a						)







      def _UpperCamelCase						(    self				:	Optional[int]						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=copy.deepcopy(self.__dict__						)
            __snake_case      :    Tuple  			=self.encoder.to_dict()
            __snake_case      :    Union[str, Any]  			=self.decoder.to_dict()
            __snake_case      :    str  			=self.__class__.model_type
            return output



class      _lowercase (     lowerCAmelCase						):
      _a   :     List[Any]								=   version.parse('''1.11'''						)


      @property
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            return OrderedDict(
                [
                    ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                ]						)


      @property
      def _UpperCamelCase						(    self				:	Tuple						):





            """simple docstring"""







            return 1e-4







      @property
      def _UpperCamelCase						(    self				:	Dict						):





            """simple docstring"""







            return OrderedDict({'''last_hidden_state''': {0: '''batch''', 1: '''encoder_sequence'''}}						)



class      _lowercase (     lowerCAmelCase						):


      @property
      def _UpperCamelCase						(    self				:	Any						):





            """simple docstring"""







            __snake_case      :    Dict  			=OrderedDict()
            __snake_case      :    Union[str, Any]  			={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
            __snake_case      :    Optional[int]  			={0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
            __snake_case      :    List[Any]  			={0: '''batch''', 1: '''encoder_sequence'''}

            return common_inputs







      def _UpperCamelCase						(    self				:	List[Any]							,   a				:	"PreTrainedTokenizerBase"							,   a				:	int = -1							,   a				:	int = -1							,   a				:	bool = False							,   a				:	Optional["TensorType"] = None							,   ):





            """simple docstring"""







            import torch

            __snake_case      :    Union[str, Any]  			=OrderedDict()

            __snake_case      :    Any  			=super().generate_dummy_inputs(
                a							,   batch_size=a							,   seq_length=a							,   is_pair=a							,   framework=a						)

            __snake_case			,				__snake_case      :    List[Any]  			=dummy_input['''input_ids'''].shape
            __snake_case      :    int  			=(batch, encoder_sequence, self._config.encoder_hidden_size)
            __snake_case      :    Dict  			=dummy_input.pop('''input_ids'''						)
            __snake_case      :    Dict  			=dummy_input.pop('''attention_mask'''						)
            __snake_case      :    Union[str, Any]  			=torch.zeros(a						)

            return common_inputs



class      _lowercase (     lowerCAmelCase						):


      @property
      def _UpperCamelCase						(    self				:	List[str]						):





            """simple docstring"""







            pass


      def _UpperCamelCase						(    self				:	Union[str, Any]							,   a				:	PretrainedConfig						):





            """simple docstring"""







            return VisionEncoderDecoderEncoderOnnxConfig(a						)







      def _UpperCamelCase						(    self				:	List[Any]							,   a				:	PretrainedConfig							,   a				:	PretrainedConfig							,   a				:	str = "default"						):





            """simple docstring"""







            __snake_case      :    Optional[Any]  			=encoder_config.hidden_size
            return VisionEncoderDecoderDecoderOnnxConfig(a							,   a						)










"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def     __lowercase					(    a :				str )					->					None:
      __snake_case			,				__snake_case      :    List[Any]  			=analyze_text(a )
      __snake_case      :    Union[str, Any]  			=list(''' ''' + ascii_lowercase )
      # what is our total sum of probabilities.
      __snake_case      :    List[Any]  			=sum(single_char_strings.values() )

      # one length string
      __snake_case      :    str  			=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      :    Union[str, Any]  			=single_char_strings[ch]
                  __snake_case      :    Any  			=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
      __snake_case      :    Optional[Any]  			=sum(two_char_strings.values() )
      __snake_case      :    Dict  			=0
      # for each alpha (two in size) calculate entropy.
      for cha in my_alphas:
            for cha in my_alphas:
                  __snake_case      :    Tuple  			=cha + cha
                  if sequence in two_char_strings:
                        __snake_case      :    List[Any]  			=two_char_strings[sequence]
                        __snake_case      :    Tuple  			=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     __lowercase					(    a :				str )					->					tuple[dict, dict]:
      __snake_case      :    List[str]  			=Counter()  # type: ignore
      __snake_case      :    Union[str, 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(a ) - 1 ):
            single_char_strings[text[i]] += 1
            two_char_strings[text[i : i + 2]] += 1
      return single_char_strings, two_char_strings



def     __lowercase					(    )					->					int:
      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()


