import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

_lowerCamelCase   :				List[str]     			=						3





def 		__lowerCamelCase							(UpperCAmelCase__       :							int					):
					print("Generating primitive root of p"					)
					while True:
										SCREAMING_SNAKE_CASE           =    random.randrange(3 ,	UpperCAmelCase__					)
										if pow(UpperCAmelCase__ ,	2 ,	UpperCAmelCase__					) == 1:
															continue
										if pow(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					) == 1:
															continue
										return g





def 		__lowerCamelCase							(UpperCAmelCase__       :							int					):
					print("Generating prime p..."					)
					SCREAMING_SNAKE_CASE           =    rabin_miller.generate_large_prime(UpperCAmelCase__					)  # select large prime number.
					SCREAMING_SNAKE_CASE           =    primitive_root(UpperCAmelCase__					)  # one primitive root on modulo p.
					SCREAMING_SNAKE_CASE           =    random.randrange(3 ,	UpperCAmelCase__					)  # private_key -> have to be greater than 2 for safety.
					SCREAMING_SNAKE_CASE           =    cryptomath.find_mod_inverse(pow(UpperCAmelCase__ ,	UpperCAmelCase__ ,	UpperCAmelCase__					) ,	UpperCAmelCase__					)

					SCREAMING_SNAKE_CASE           =    (key_size, e_a, e_a, p)
					SCREAMING_SNAKE_CASE           =    (key_size, d)

					return public_key, private_key





def 		__lowerCamelCase							(UpperCAmelCase__       :							str ,	UpperCAmelCase__       :							int					):
					if os.path.exists(F"{name}_pubkey.txt"					) or os.path.exists(F"{name}_privkey.txt"					):
										print("\nWARNING:"					)
										print(
										    F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
										    "Use a different name or delete these files and re-run this program."					)
										sys.exit()

					SCREAMING_SNAKE_CASE     ,				SCREAMING_SNAKE_CASE           =    generate_key(UpperCAmelCase__					)
					print(F"\nWriting public key to file {name}_pubkey.txt..."					)
					with open(F"{name}_pubkey.txt" ,	"w"					) as fo:
										fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}"					)

					print(F"Writing private key to file {name}_privkey.txt..."					)
					with open(F"{name}_privkey.txt" ,	"w"					) as fo:
										fo.write(F"{private_key[0]},{private_key[1]}"					)





def 		__lowerCamelCase							():
					print("Making key files..."					)
					make_key_files("elgamal" ,	2_0_4_8					)
					print("Key files generation successful"					)


if __name__ == "__main__":
							main()





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


_lowerCamelCase   :				Union[str, Any]     			=						logging.get_logger(__name__)


_lowerCamelCase   :				str     			=						{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_lowerCamelCase   :				int     			=						{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

_lowerCamelCase   :				List[Any]     			=						{
    '''allenai/led-base-16384''': 1_63_84,
}


class   lowercase      (       a       ):
							lowercase__  :    Tuple									=							VOCAB_FILES_NAMES
							lowercase__  :    Any									=							PRETRAINED_VOCAB_FILES_MAP
							lowercase__  :    Optional[Any]									=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase__  :    Optional[Any]									=							LEDTokenizer
							lowercase__  :    str									=							["""input_ids""", """attention_mask"""]




							def __init__(  self				:   Union[str, Any]		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   str=None		,							_UpperCamelCase				:   Tuple=None		,							_UpperCamelCase				:   List[str]="replace"		,							_UpperCamelCase				:   str="<s>"		,							_UpperCamelCase				:   List[Any]="</s>"		,							_UpperCamelCase				:   List[Any]="</s>"		,							_UpperCamelCase				:   List[str]="<s>"		,							_UpperCamelCase				:   Tuple="<unk>"		,							_UpperCamelCase				:   List[Any]="<pad>"		,							_UpperCamelCase				:   Tuple="<mask>"		,							_UpperCamelCase				:   List[str]=False		,							_UpperCamelCase				:   List[Any]=True		,							**_UpperCamelCase				:   Optional[Any]		,							)   ->			Tuple:



												'''simple docstring'''

												super().__init__(
												    _UpperCamelCase		,							_UpperCamelCase		,							tokenizer_file=_UpperCamelCase		,							errors=_UpperCamelCase		,							bos_token=_UpperCamelCase		,							eos_token=_UpperCamelCase		,							sep_token=_UpperCamelCase		,							cls_token=_UpperCamelCase		,							unk_token=_UpperCamelCase		,							pad_token=_UpperCamelCase		,							mask_token=_UpperCamelCase		,							add_prefix_space=_UpperCamelCase		,							trim_offsets=_UpperCamelCase		,							**_UpperCamelCase		,							)

												SCREAMING_SNAKE_CASE           =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
												if pre_tok_state.get("add_prefix_space"		,							_UpperCamelCase ) != add_prefix_space:
																	SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							pre_tok_state.pop("type" ) )
																	SCREAMING_SNAKE_CASE           =    add_prefix_space
																	SCREAMING_SNAKE_CASE           =    pre_tok_class(**_UpperCamelCase )

												SCREAMING_SNAKE_CASE           =    add_prefix_space

												# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
												SCREAMING_SNAKE_CASE           =    "post_processor"
												SCREAMING_SNAKE_CASE           =    getattr(self.backend_tokenizer		,							_UpperCamelCase		,							_UpperCamelCase )
												if tokenizer_component_instance:
																	SCREAMING_SNAKE_CASE           =    json.loads(tokenizer_component_instance.__getstate__() )

																	# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
																	if "sep" in state:
																						SCREAMING_SNAKE_CASE           =    tuple(state["sep"] )
																	if "cls" in state:
																						SCREAMING_SNAKE_CASE           =    tuple(state["cls"] )

																	SCREAMING_SNAKE_CASE           =    False

																	if state.get("add_prefix_space"		,							_UpperCamelCase ) != add_prefix_space:
																						SCREAMING_SNAKE_CASE           =    add_prefix_space
																						SCREAMING_SNAKE_CASE           =    True

																	if state.get("trim_offsets"		,							_UpperCamelCase ) != trim_offsets:
																						SCREAMING_SNAKE_CASE           =    trim_offsets
																						SCREAMING_SNAKE_CASE           =    True

																	if changes_to_apply:
																						SCREAMING_SNAKE_CASE           =    getattr(_UpperCamelCase		,							state.pop("type" ) )
																						SCREAMING_SNAKE_CASE           =    component_class(**_UpperCamelCase )
																						setattr(self.backend_tokenizer		,							_UpperCamelCase		,							_UpperCamelCase )




							@property
							# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
							def 		__snake_case(  self				:   int )   ->			str:



												'''simple docstring'''

												if self._mask_token is None:
																	if self.verbose:
																						logger.error("Using mask_token, but it is not set yet." )
																	return None
												return str(self._mask_token )




							@mask_token.setter
							def 		__snake_case(  self				:   Optional[int]		,							_UpperCamelCase				:   Optional[int] )   ->			Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    AddedToken(_UpperCamelCase		,							lstrip=_UpperCamelCase		,							rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase		,							_UpperCamelCase ) else value
												SCREAMING_SNAKE_CASE           =    value




							def 		__snake_case(  self				:   List[Any]		,							*_UpperCamelCase				:   Optional[int]		,							**_UpperCamelCase				:   Union[str, Any] )   ->			BatchEncoding:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    kwargs.get("is_split_into_words"		,							_UpperCamelCase )

												if is_split_into_words and not self.add_prefix_space:
																	raise ValueError(
																	    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
																	    "to use it with pretokenized inputs." )

												return super()._batch_encode_plus(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   int		,							*_UpperCamelCase				:   Dict		,							**_UpperCamelCase				:   Tuple )   ->			BatchEncoding:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    kwargs.get("is_split_into_words"		,							_UpperCamelCase )

												if is_split_into_words and not self.add_prefix_space:
																	raise ValueError(
																	    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
																	    "to use it with pretokenized inputs." )

												return super()._encode_plus(*_UpperCamelCase		,							**_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   str		,							_UpperCamelCase				:   Optional[str] = None )   ->			Tuple[str]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    self._tokenizer.model.save(_UpperCamelCase		,							name=_UpperCamelCase )
												return tuple(_UpperCamelCase )




							def 		__snake_case(  self				:   List[Any]		,							_UpperCamelCase				:   List[Any]		,							_UpperCamelCase				:   int=None )   ->			List[Any]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																	return output

												return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]




							def 		__snake_case(  self				:   Dict		,							_UpperCamelCase				:   List[int]		,							_UpperCamelCase				:   Optional[List[int]] = None )   ->			List[int]:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    [self.sep_token_id]
												SCREAMING_SNAKE_CASE           =    [self.cls_token_id]

												if token_ids_a is None:
																	return len(cls + token_ids_a + sep ) * [0]
												return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]





							def 		__snake_case(  self				:   Optional[Any]		,							_UpperCamelCase				:   Union[Dict[str, EncodedInput], BatchEncoding]		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD		,							_UpperCamelCase				:   Optional[int] = None		,							_UpperCamelCase				:   Optional[bool] = None		,							)   ->			dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE           =    super()._pad(
												    encoded_inputs=_UpperCamelCase		,							max_length=_UpperCamelCase		,							padding_strategy=_UpperCamelCase		,							pad_to_multiple_of=_UpperCamelCase		,							return_attention_mask=_UpperCamelCase		,							)

												# Load from model defaults
												if return_attention_mask is None:
																	SCREAMING_SNAKE_CASE           =    "attention_mask" in self.model_input_names

												if return_attention_mask and "global_attention_mask" in encoded_inputs:
																	SCREAMING_SNAKE_CASE           =    encoded_inputs[self.model_input_names[0]]
																	# `global_attention_mask` need to have the same length as other (sequential) inputs.
																	SCREAMING_SNAKE_CASE           =    len(encoded_inputs["global_attention_mask"] ) != len(_UpperCamelCase )

																	if needs_to_be_padded:
																						SCREAMING_SNAKE_CASE           =    len(_UpperCamelCase ) - len(encoded_inputs["global_attention_mask"] )

																						if self.padding_side == "right":
																											# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																											SCREAMING_SNAKE_CASE           =    (
																											    encoded_inputs["global_attention_mask"] + [-1] * difference
																											)
																						elif self.padding_side == "left":
																											SCREAMING_SNAKE_CASE           =    [-1] * difference + encoded_inputs[
																											    "global_attention_mask"
																											]
																						else:
																											raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )

												return encoded_inputs




