


import string

import numpy


def     lowercase	(    a     , a ):



		'''simple docstring'''




		return b if a == 0 else greatest_common_divisor(b % a     , a )



class 						_UpperCAmelCase      :
		lowerCamelCase_	:			Union[str, Any]       			=  string.ascii_uppercase + string.digits
		# This cipher takes alphanumerics into account
		# i.e. a total of 36 characters

		# take x and return x % len(key_string)
		lowerCamelCase_	:			Optional[int]       			=  numpy.vectorize(lambda lowercase							: x % 3_6							)

		lowerCamelCase_	:			List[Any]       			=  numpy.vectorize(lowercase							)






		def __init__(						self				:     Any   ,  UpperCAmelCase				:     numpy.ndarray):
				SCREAMING_SNAKE_CASE_				:Any				     =						self.modulus(UpperCAmelCase)  # mod36 calc's on the encrypt key
				self.check_determinant()  # validate the determinant of the encryption key
				SCREAMING_SNAKE_CASE_				:str				     =						encrypt_key.shape[0]






		def _snake_case		(						self				:     Tuple   ,  UpperCAmelCase				:     str):
				return self.key_string.index(UpperCAmelCase)






		def _snake_case		(						self				:     List[str]   ,  UpperCAmelCase				:     int):
				return self.key_string[round(UpperCAmelCase)]






		def _snake_case		(						self				:     List[str]):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						round(numpy.linalg.det(self.encrypt_key))

				if det < 0:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						det % len(self.key_string)

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						len(self.key_string)
				if greatest_common_divisor(UpperCAmelCase   ,  len(self.key_string)) != 1:
						SCREAMING_SNAKE_CASE_				:Any				     =						(
						    F"determinant modular {req_l} of encryption key({det}) "
						    F"is not co prime w.r.t {req_l}.\nTry another key."
						)
						raise ValueError(UpperCAmelCase)






		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:List[str]				     =						[char for char in text.upper() if char in self.key_string]

				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						chars[-1]
				while len(UpperCAmelCase) % self.break_key != 0:
						chars.append(UpperCAmelCase)

				return "".join(UpperCAmelCase)






		def _snake_case		(						self				:     str   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.process_text(text.upper())
				SCREAMING_SNAKE_CASE_				:Any				     =						""

				for i in range(0   ,  len(UpperCAmelCase) - self.break_key + 1   ,  self.break_key):
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						text[i : i + self.break_key]
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						[self.replace_letters(UpperCAmelCase) for char in batch]
						SCREAMING_SNAKE_CASE_				:Tuple				     =						numpy.array([vec]).T
						SCREAMING_SNAKE_CASE_				:int				     =						self.modulus(self.encrypt_key.dot(UpperCAmelCase)).T.tolist()[
						    0
						]
						SCREAMING_SNAKE_CASE_				:List[str]				     =						"".join(
						    self.replace_digits(UpperCAmelCase) for num in batch_encrypted)
						encrypted += encrypted_batch

				return encrypted






		def _snake_case		(						self				:     Any):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						round(numpy.linalg.det(self.encrypt_key))

				if det < 0:
						SCREAMING_SNAKE_CASE_				:str				     =						det % len(self.key_string)
				SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						None
				for i in range(len(self.key_string)):
						if (det * i) % len(self.key_string) == 1:
								SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						i
								break

				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						(
				    det_inv
				    * numpy.linalg.det(self.encrypt_key)
				    * numpy.linalg.inv(self.encrypt_key)
				)

				return self.to_int(self.modulus(UpperCAmelCase))







		def _snake_case		(						self				:     List[Any]   ,  UpperCAmelCase				:     str):
				SCREAMING_SNAKE_CASE_				:int				     =						self.make_decrypt_key()
				SCREAMING_SNAKE_CASE_				:List[Any]				     =						self.process_text(text.upper())
				SCREAMING_SNAKE_CASE_				:str				     =						""

				for i in range(0   ,  len(UpperCAmelCase) - self.break_key + 1   ,  self.break_key):
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						text[i : i + self.break_key]
						SCREAMING_SNAKE_CASE_				:Any				     =						[self.replace_letters(UpperCAmelCase) for char in batch]
						SCREAMING_SNAKE_CASE_				:int				     =						numpy.array([vec]).T
						SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						self.modulus(decrypt_key.dot(UpperCAmelCase)).T.tolist()[0]
						SCREAMING_SNAKE_CASE_				:List[str]				     =						"".join(
						    self.replace_digits(UpperCAmelCase) for num in batch_decrypted)
						decrypted += decrypted_batch

				return decrypted




def     lowercase	(    ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Tuple				     =						int(input("Enter the order of the encryption key: " ) )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						[]

		print("Enter each row of the encryption key with space separated integers" )
		for _ in range(a ):
				SCREAMING_SNAKE_CASE_				:Tuple				     =						[int(a ) for x in input().split()]
				hill_matrix.append(a )

		SCREAMING_SNAKE_CASE_				:List[str]				     =						HillCipher(numpy.array(a ) )

		print("Would you like to encrypt or decrypt some text? (1 or 2)" )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						input("\n1. Encrypt\n2. Decrypt\n" )
		if option == "1":
				SCREAMING_SNAKE_CASE_				:Any				     =						input("What text would you like to encrypt?: " )
				print("Your encrypted text is:" )
				print(hc.encrypt(a ) )
		elif option == "2":
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						input("What text would you like to decrypt?: " )
				print("Your decrypted text is:" )
				print(hc.decrypt(a ) )


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		main()










import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


SCREAMING_SNAKE_CASE__     					=			datasets.logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__     					=			"\\n@inproceedings{bleurt,\n  title={BLEURT: Learning Robust Metrics for Text Generation},\n  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},\n  booktitle={ACL},\n  year={2020},\n  url={https://arxiv.org/abs/2004.04696}\n}\n"

SCREAMING_SNAKE_CASE__     					=			"\\nBLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)\nand then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune\nit for your specific application (the latter is expected to perform better).\n\nSee the project's README at https://github.com/google-research/bleurt#readme for more information.\n"

SCREAMING_SNAKE_CASE__     					=			"\nBLEURT score.\n\nArgs:\n    `predictions` (list of str): prediction/candidate sentences\n    `references` (list of str): reference sentences\n    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.\n\nReturns:\n    'scores': List of scores.\nExamples:\n\n    >>> predictions = [\"hello there\", \"general kenobi\"]\n    >>> references = [\"hello there\", \"general kenobi\"]\n    >>> bleurt = datasets.load_metric(\"bleurt\")\n    >>> results = bleurt.compute(predictions=predictions, references=references)\n    >>> print([round(v, 2) for v in results[\"scores\"]])\n    [1.03, 1.04]\n"

SCREAMING_SNAKE_CASE__     					=			{
    "bleurt-tiny-128": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip",
    "bleurt-tiny-512": "https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip",
    "bleurt-base-128": "https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip",
    "bleurt-base-512": "https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip",
    "bleurt-large-128": "https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip",
    "bleurt-large-512": "https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip",
    "BLEURT-20-D3": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip",
    "BLEURT-20-D6": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip",
    "BLEURT-20-D12": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip",
    "BLEURT-20": "https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip",
}



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION							)
class 						_UpperCAmelCase      (     datasets.Metric							):






		def _snake_case		(						self				:     List[str]):
				return datasets.MetricInfo(
				    description=_DESCRIPTION   ,  citation=_CITATION   ,  homepage="https://github.com/google-research/bleurt"   ,  inputs_description=_KWARGS_DESCRIPTION   ,  features=datasets.Features(
				        {
				            "predictions": datasets.Value("string"   ,  id="sequence"),
				            "references": datasets.Value("string"   ,  id="sequence"),
				        })   ,  codebase_urls=["https://github.com/google-research/bleurt"]   ,  reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]   ,  )






		def _snake_case		(						self				:     Any   ,  UpperCAmelCase				:     Any):
				# check that config name specifies a valid BLEURT model
				if self.config_name == "default":
						logger.warning(
						    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
						    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').")
						SCREAMING_SNAKE_CASE_				:str				     =						"bleurt-base-128"

				if self.config_name.lower() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.config_name.lower()

				elif self.config_name.upper() in CHECKPOINT_URLS:
						SCREAMING_SNAKE_CASE_				:Dict				     =						self.config_name.upper()

				else:
						raise KeyError(
						    F"{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}")

				# download the model checkpoint specified by self.config_name and set up the scorer
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name])
				SCREAMING_SNAKE_CASE_				:Dict				     =						score.BleurtScorer(os.path.join(UpperCAmelCase   ,  UpperCAmelCase))







		def _snake_case		(						self				:     Union[str, Any]   ,  UpperCAmelCase				:     List[str]   ,  UpperCAmelCase				:     Union[str, Any]):
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						self.scorer.score(references=UpperCAmelCase   ,  candidates=UpperCAmelCase)
				return {"scores": scores}






