





from ...configuration_utils import PretrainedConfig
from ...utils import logging


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

_lowerCamelCase :    Any  							= {
    '''google/vivit-b-16x2-kinetics400''': (
        '''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'''
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}







class 						lowerCamelCase       (__lowerCamelCase      ):







							"""simple docstring"""




							UpperCAmelCase_      						=   "vivit"






							def __init__( self     :					List[str],			_UpperCAmelCase     :					Optional[int]=2_2_4,			_UpperCAmelCase     :					Any=3_2,			_UpperCAmelCase     :					Dict=[2, 1_6, 1_6],			_UpperCAmelCase     :					int=3,			_UpperCAmelCase     :					List[Any]=7_6_8,			_UpperCAmelCase     :					int=1_2,			_UpperCAmelCase     :					Tuple=1_2,			_UpperCAmelCase     :					Any=3_0_7_2,			_UpperCAmelCase     :					Tuple="gelu_fast",			_UpperCAmelCase     :					Dict=0.0,			_UpperCAmelCase     :					List[str]=0.0,			_UpperCAmelCase     :					Tuple=0.02,			_UpperCAmelCase     :					List[Any]=1E-06,			_UpperCAmelCase     :					Dict=True,			**_UpperCAmelCase     :					Optional[Any],			)				->			Dict:





														"""simple docstring"""
														SCREAMING_SNAKE_CASE__       :      Tuple     =						hidden_size
														SCREAMING_SNAKE_CASE__       :      Optional[Any]     =						num_hidden_layers
														SCREAMING_SNAKE_CASE__       :      Dict     =						num_attention_heads
														SCREAMING_SNAKE_CASE__       :      Any     =						intermediate_size
														SCREAMING_SNAKE_CASE__       :      Dict     =						hidden_act
														SCREAMING_SNAKE_CASE__       :      Dict     =						hidden_dropout_prob
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						attention_probs_dropout_prob
														SCREAMING_SNAKE_CASE__       :      List[str]     =						initializer_range
														SCREAMING_SNAKE_CASE__       :      Tuple     =						layer_norm_eps

														SCREAMING_SNAKE_CASE__       :      Optional[int]     =						image_size
														SCREAMING_SNAKE_CASE__       :      str     =						num_frames
														SCREAMING_SNAKE_CASE__       :      Union[str, Any]     =						tubelet_size
														SCREAMING_SNAKE_CASE__       :      str     =						num_channels
														SCREAMING_SNAKE_CASE__       :      int     =						qkv_bias

														super().__init__(**_UpperCAmelCase						)













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()






