





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


_lowerCamelCase :    Dict  							= {
    '''configuration_groupvit''': [
        '''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''GroupViTConfig''',
        '''GroupViTOnnxConfig''',
        '''GroupViTTextConfig''',
        '''GroupViTVisionConfig''',
    ],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase :    Any  							= [
		    '''GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''GroupViTModel''',
		    '''GroupViTPreTrainedModel''',
		    '''GroupViTTextModel''',
		    '''GroupViTVisionModel''',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowerCamelCase :    str  							= [
		    '''TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFGroupViTModel''',
		    '''TFGroupViTPreTrainedModel''',
		    '''TFGroupViTTextModel''',
		    '''TFGroupViTVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_groupvit import (
		    GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    GroupViTConfig,
		    GroupViTOnnxConfig,
		    GroupViTTextConfig,
		    GroupViTVisionConfig,
		)

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_groupvit import (
				    GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    GroupViTModel,
				    GroupViTPreTrainedModel,
				    GroupViTTextModel,
				    GroupViTVisionModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_groupvit import (
				    TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFGroupViTModel,
				    TFGroupViTPreTrainedModel,
				    TFGroupViTTextModel,
				    TFGroupViTVisionModel,
				)

else:
		import sys

		_lowerCamelCase :    Optional[Any]  							= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)













from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase




def _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    None:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__       :      Dict     =						analyze_text(SCREAMING_SNAKE_CASE__       )
							SCREAMING_SNAKE_CASE__       :      Tuple     =						list(" " + ascii_lowercase       )
							# what is our total sum of probabilities.
							SCREAMING_SNAKE_CASE__       :      str     =						sum(single_char_strings.values()       )

							# one length string
							SCREAMING_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:
																					SCREAMING_SNAKE_CASE__       :      Optional[int]     =						single_char_strings[ch]
																					SCREAMING_SNAKE_CASE__       :      Any     =						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
							SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						sum(two_char_strings.values()       )
							SCREAMING_SNAKE_CASE__       :      int     =						0
							# for each alpha (two in size) calculate entropy.
							for cha in my_alphas:
														for cha in my_alphas:
																					SCREAMING_SNAKE_CASE__       :      List[str]     =						cha + cha
																					if sequence in two_char_strings:
																												SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						two_char_strings[sequence]
																												SCREAMING_SNAKE_CASE__       :      Tuple     =						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 _a (  SCREAMING_SNAKE_CASE__      :  str       )	->    tuple[dict, dict]:

							'''simple docstring'''






							SCREAMING_SNAKE_CASE__       :      str     =						Counter()  # type: ignore
							SCREAMING_SNAKE_CASE__       :      Dict     =						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 _a (  )	->    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()






