



'''simple docstring'''

import argparse

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging


logging.set_verbosity_info()
a							   =				logging.get_logger(__name__)







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Union[str, Any]:





			"""simple docstring"""







			snake_case:    Dict      							=RobertaPreLayerNormConfig.from_pretrained(
			    __lowerCamelCase				,							architectures=['RobertaPreLayerNormForMaskedLM']					)

			# convert state_dict
			snake_case:    List[str]      							=torch.load(hf_hub_download(repo_id=__lowerCamelCase				,							filename='pytorch_model.bin'					)					)
			snake_case:    int      							={}
			for tensor_key, tensor_value in original_state_dict.items():
						# The transformer implementation gives the model a unique name, rather than overwiriting 'roberta'
						if tensor_key.startswith('roberta.'					):
									snake_case:    Dict      							='roberta_prelayernorm.' + tensor_key[len('roberta.'					) :]

						# The original implementation contains weights which are not used, remove them from the state_dict
						if tensor_key.endswith('.self.LayerNorm.weight'					) or tensor_key.endswith('.self.LayerNorm.bias'					):
									continue

						snake_case:    int      							=tensor_value

			snake_case:    Optional[Any]      							=RobertaPreLayerNormForMaskedLM.from_pretrained(
			    pretrained_model_name_or_path=__lowerCamelCase				,							config=__lowerCamelCase				,							state_dict=__lowerCamelCase					)
			model.save_pretrained(__lowerCamelCase					)

			# convert tokenizer
			snake_case:    List[Any]      							=AutoTokenizer.from_pretrained(__lowerCamelCase					)
			tokenizer.save_pretrained(__lowerCamelCase					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--checkpoint-repo',
	    default=None,
	    type=str,
	    required=True,
	    help='Path the official PyTorch dump, e.g. \'andreasmadsen/efficient_mlm_m0.40\'.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	a							   =				parser.parse_args()
	convert_roberta_prelayernorm_checkpoint_to_pytorch(args.checkpoint_repo, args.pytorch_dump_folder_path)









from __future__ import annotations

import math
from collections import Counter
from string import ascii_lowercase






def        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    None:
					__lowerCAmelCase ,						__lowerCAmelCase						 =analyze_text(__lowerCamelCase						)
					__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(__lowerCamelCase						)  # 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(__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        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    tuple[dict, dict]:
					__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(__lowerCamelCase						) - 1						):
										single_char_strings[text[i]] += 1
										two_char_strings[text[i : i + 2]] += 1
					return single_char_strings, two_char_strings






def        __lowerCAmelCase		(	)	->    str:
					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()




