
"""simple docstring"""





# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_a														=		{
    """configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		["""VivitImageProcessor"""]


try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """VivitModel""",
				    """VivitPreTrainedModel""",
				    """VivitForVideoClassification""",
				]


if TYPE_CHECKING:
				from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_vivit import VivitImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_vivit import (
								    VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    VivitForVideoClassification,
								    VivitModel,
								    VivitPreTrainedModel,
								)


else:
				import sys

				_a														=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)








"""simple docstring"""





from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase



def 				lowerCamelCase__		(      __snake_case     )							->		None:


					"""simple docstring"""





					_UpperCamelCase			,  _UpperCamelCase										=  analyze_text(__snake_case     )
					_UpperCamelCase										=  list(''' ''' + ascii_lowercase     )
					# what is our total sum of probabilities.
					_UpperCamelCase										=  sum(single_char_strings.values()     )

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

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

					# two len string
					_UpperCamelCase										=  sum(two_char_strings.values()     )
					_UpperCamelCase										=  0
					# for each alpha (two in size) calculate entropy.
					for cha in my_alphas:
										for cha in my_alphas:
															_UpperCamelCase										=  cha + cha
															if sequence in two_char_strings:
																				_UpperCamelCase										=  two_char_strings[sequence]
																				_UpperCamelCase										=  int(__snake_case     ) / all_sum
																				my_sec_sum += prob * math.loga(__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__		(      __snake_case     )							->		tuple[dict, dict]:


					"""simple docstring"""





					_UpperCamelCase										=  Counter()  # type: ignore
					_UpperCamelCase										=  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(__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__		(      )							->		Dict:


					"""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()






