



"""simple docstring"""
from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
       import timm


if is_torch_available():
       from torch import Tensor


class    SCREAMING_SNAKE_CASE__ (							_snake_case      ,  _snake_case   ):
    lowercase__      =   """pixel_values"""
    lowercase__      =   False
    lowercase__      =   TimmBackboneConfig

    def __init__(				self			:				Any ,  lowerCAmelCase_			:				Optional[Any] ,  **lowerCAmelCase_			:				str):







      """simple docstring"""







      requires_backends(self ,  """timm""")
      super().__init__(snake_case_)
      lowercase_								= config

      if config.backbone is None:
        raise ValueError("""backbone is not set in the config. Please set it to a timm model name.""")

      if config.backbone not in timm.list_models():
        raise ValueError(F'''backbone {config.backbone} is not supported by timm.''')

      if hasattr(snake_case_ ,  """out_features""") and config.out_features is not None:
        raise ValueError("""out_features is not supported by TimmBackbone. Please use out_indices instead.""")

      lowercase_								= getattr(snake_case_ ,  """use_pretrained_backbone""" ,  snake_case_)
      if pretrained is None:
        raise ValueError("""use_pretrained_backbone is not set in the config. Please set it to True or False.""")

      # We just take the final layer by default. This matches the default for the transformers models.
      lowercase_								= config.out_indices if getattr(snake_case_ ,  """out_indices""" ,  snake_case_) is not None else (-1,)

      lowercase_								= timm.create_model(
          config.backbone ,  pretrained=snake_case_ ,  features_only=config.features_only ,  in_chans=config.num_channels ,  out_indices=snake_case_ ,  **snake_case_ ,  )
      # These are used to control the output of the model when called. If output_hidden_states is True, then
      # return_layers is modified to include all layers.
      lowercase_								= self._backbone.return_layers
      lowercase_								= {layer["module"]: str(snake_case_) for i, layer in enumerate(self._backbone.feature_info.info)}
      super()._init_backbone(snake_case_)

    @classmethod
    def 					_UpperCAmelCase	(				cls			:				Tuple ,  lowerCAmelCase_			:				str ,  *lowerCAmelCase_			:				List[str] ,  **lowerCAmelCase_			:				Optional[int]):







      """simple docstring"""







      requires_backends(cls ,  ["""vision""", """timm"""])
      from ...models.timm_backbone import TimmBackboneConfig

      lowercase_								= kwargs.pop("""config""" ,  TimmBackboneConfig())

      lowercase_								= kwargs.pop("""use_timm_backbone""" ,  snake_case_)
      if not use_timm:
        raise ValueError("""use_timm_backbone must be True for timm backbones""")

      lowercase_								= kwargs.pop("""num_channels""" ,  config.num_channels)
      lowercase_								= kwargs.pop("""features_only""" ,  config.features_only)
      lowercase_								= kwargs.pop("""use_pretrained_backbone""" ,  config.use_pretrained_backbone)
      lowercase_								= kwargs.pop("""out_indices""" ,  config.out_indices)
      lowercase_								= TimmBackboneConfig(
          backbone=snake_case_ ,  num_channels=snake_case_ ,  features_only=snake_case_ ,  use_pretrained_backbone=snake_case_ ,  out_indices=snake_case_ ,  )
      return super()._from_config(snake_case_ ,  **snake_case_)

    def 					_UpperCAmelCase	(				self			:				str ,  lowerCAmelCase_			:				Union[str, Any]):







      """simple docstring"""







      pass




    def 					_UpperCAmelCase	(				self			:				Dict ,  lowerCAmelCase_			:				Union[str, Any] ,  lowerCAmelCase_			:				int=None ,  lowerCAmelCase_			:				Dict=None ,  lowerCAmelCase_			:				Tuple=None ,  **lowerCAmelCase_			:				List[Any]):







      """simple docstring"""







      lowercase_								= return_dict if return_dict is not None else self.config.use_return_dict
      lowercase_								= (
          output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
      )
      lowercase_								= output_attentions if output_attentions is not None else self.config.output_attentions

      if output_attentions:
        raise ValueError("""Cannot output attentions for timm backbones at the moment""")

      if output_hidden_states:
        # We modify the return layers to include all the stages of the backbone
        lowercase_								= self._all_layers
        lowercase_								= self._backbone(snake_case_ ,  **snake_case_)
        lowercase_								= self._return_layers
        lowercase_								= tuple(hidden_states[i] for i in self.out_indices)
      else:
        lowercase_								= self._backbone(snake_case_ ,  **snake_case_)
        lowercase_								= None

      lowercase_								= tuple(snake_case_)
      lowercase_								= tuple(snake_case_) if hidden_states is not None else None

      if not return_dict:
        lowercase_								= (feature_maps,)
        if output_hidden_states:
          lowercase_								= output + (hidden_states,)
        return output

      return BackboneOutput(feature_maps=snake_case_ ,  hidden_states=snake_case_ ,  attentions=snake_case_)






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

import math
from collections import Counter
from string import ascii_lowercase


def 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						None:







  '''simple docstring'''


  lowercase_					,    lowercase_								= analyze_text(__lowerCAmelCase							)
  lowercase_								= list(""" """ + ascii_lowercase							)
  # what is our total sum of probabilities.
  lowercase_								= sum(single_char_strings.values()							)

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

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

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

    # 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 			_SCREAMING_SNAKE_CASE						(__lowerCAmelCase							) ->						tuple[dict, dict]:







  '''simple docstring'''


  lowercase_								= Counter()  # type: ignore
  lowercase_								= 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(__lowerCAmelCase							) - 1							):
    single_char_strings[text[i]] += 1
    two_char_strings[text[i : i + 2]] += 1
  return single_char_strings, two_char_strings


def 			_SCREAMING_SNAKE_CASE						() ->						str:







  '''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()

