
"""simple docstring"""



from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def 						__snake_case	(     SCREAMING_SNAKE_CASE:       str    ):






							"""simple docstring"""



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

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

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

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

    # 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 						__snake_case	(     SCREAMING_SNAKE_CASE:       str    ):






							"""simple docstring"""



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






def 						__snake_case	(     ):






							"""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()













import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()


def       UpperCamelCase__(			UpperCamelCase__     :			Any					,	UpperCamelCase__     :			int					,	UpperCamelCase__     :			List[Any]    )->Optional[int]:
       # Initialise PyTorch model
       A__		   =		RemBertConfig.from_json_file(UpperCamelCase__    )
       print('''Building PyTorch model from configuration: {}'''.format(str(UpperCamelCase__    )    )    )
       A__		   =		RemBertModel(UpperCamelCase__    )

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__    )

       # Save pytorch-model
       print('''Save PyTorch model to {}'''.format(UpperCamelCase__    )    )
       torch.save(model.state_dict()					,	UpperCamelCase__    )


if __name__ == "__main__":
       a__: List[Any]							   =						argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
       )
       parser.add_argument(
           '--rembert_config_file',
           default=None,
           type=str,
           required=True,
           help=(
               'The config json file corresponding to the pre-trained RemBERT model. \n'
               'This specifies the model architecture.'
           ),
       )
       parser.add_argument(
           '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
       )
       a__: Union[str, Any]							   =						parser.parse_args()
       convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)





