




'''simple docstring'''


from __future__ import annotations
def 	snake_case_					(UpperCamelCase :						Optional[Any]				,		UpperCamelCase :						Union[str, Any] = None				,		UpperCamelCase :						Union[str, Any] = None				,		UpperCamelCase :						int = False				,		):





							'''simple docstring'''


							_a			  =       cipher_alphabet or [chr(UpperCAmelCase__   ) for i in range(97				,		123   )]

							# If the argument is None or the user provided an empty dictionary
							if not frequencies_dict:
														# Frequencies of letters in the english language (how much they show up)
														_a			  =       {
														    '''a''': 0.08497,
														    '''b''': 0.01492,
														    '''c''': 0.02202,
														    '''d''': 0.04253,
														    '''e''': 0.11162,
														    '''f''': 0.02228,
														    '''g''': 0.02015,
														    '''h''': 0.06094,
														    '''i''': 0.07546,
														    '''j''': 0.00153,
														    '''k''': 0.01292,
														    '''l''': 0.04025,
														    '''m''': 0.02406,
														    '''n''': 0.06749,
														    '''o''': 0.07507,
														    '''p''': 0.01929,
														    '''q''': 0.00095,
														    '''r''': 0.07587,
														    '''s''': 0.06327,
														    '''t''': 0.09356,
														    '''u''': 0.02758,
														    '''v''': 0.00978,
														    '''w''': 0.02560,
														    '''x''': 0.00150,
														    '''y''': 0.01994,
														    '''z''': 0.00077,
														}
							else:
														# Custom frequencies dictionary
														_a			  =       frequencies_dict

							if not case_sensitive:
														_a			  =       ciphertext.lower()

							# Chi squared statistic values
							_a			  =       {}

							# cycle through all of the shifts
							for shift in range(len(UpperCAmelCase__   )   ):
														_a			  =       ''''''

														# decrypt the message with the shift
														for letter in ciphertext:
																					try:
																												# Try to index the letter in the alphabet
																												_a			  =       (alphabet_letters.index(letter.lower()   ) - shift) % len(
																												    UpperCAmelCase__   )
																												decrypted_with_shift += (
																												    alphabet_letters[new_key].upper()
																												    if case_sensitive and letter.isupper()
																												    else alphabet_letters[new_key]
																												)
																					except ValueError:
																												# Append the character if it isn't in the alphabet
																												decrypted_with_shift += letter

														_a			  =       0.0

														# Loop through each letter in the decoded message with the shift
														for letter in decrypted_with_shift:
																					if case_sensitive:
																												_a			  =       letter.lower()
																												if letter in frequencies:
																																			# Get the amount of times the letter occurs in the message
																																			_a			  =       decrypted_with_shift.lower().count(UpperCAmelCase__   )

																																			# Get the excepcted amount of times the letter should appear based
																																			# on letter frequencies
																																			_a			  =       frequencies[letter] * occurrences

																																			# Complete the chi squared statistic formula
																																			_a			  =       ((occurrences - expected) ** 2) / expected

																																			# Add the margin of error to the total chi squared statistic
																																			chi_squared_statistic += chi_letter_value
																					else:
																												if letter.lower() in frequencies:
																																			# Get the amount of times the letter occurs in the message
																																			_a			  =       decrypted_with_shift.count(UpperCAmelCase__   )

																																			# Get the excepcted amount of times the letter should appear based
																																			# on letter frequencies
																																			_a			  =       frequencies[letter] * occurrences

																																			# Complete the chi squared statistic formula
																																			_a			  =       ((occurrences - expected) ** 2) / expected

																																			# Add the margin of error to the total chi squared statistic
																																			chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
														_a			  =       (
														    chi_squared_statistic,
														    decrypted_with_shift,
														)

							# Get the most likely cipher by finding the cipher with the smallest chi squared
							# statistic
							def chi_squared_statistic_values_sorting_key(UpperCamelCase :						Dict   ) -> tuple[float, str]:
														return chi_squared_statistic_values[key]

							_a			  =       min(
							    UpperCAmelCase__				,		key=UpperCAmelCase__				,		)

							# Get all the data from the most likely cipher (key, decoded message)
							(
							    (
							    _a	
							)  ,							(
							    _a	
							)  ,								
							)			  =       chi_squared_statistic_values[most_likely_cipher]

							# Return the data on the most likely shift
							return (
							    most_likely_cipher,
							    most_likely_cipher_chi_squared_value,
							    decoded_most_likely_cipher,
							)







'''simple docstring'''


import os

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

import datasets


_snake_case   :   Any     							=	datasets.logging.get_logger(__name__)


_snake_case   :   Tuple     							=	'\\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'

_snake_case   :   Optional[Any]     							=	'\\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'

_snake_case   :   Optional[int]     							=	'\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'

_snake_case   :   Dict     							=	{
    '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     A					(	datasets.Metric					):







							def      __lowerCAmelCase    (						self :						int      )    ->							Dict:


														"""simple docstring"""


														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      __lowerCAmelCase    (						self :						Dict							,    lowerCAmelCase_ :						str      )    ->							Tuple:


														"""simple docstring"""


														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\').'''      )
																					_a			  =       '''bleurt-base-128'''

														if self.config_name.lower() in CHECKPOINT_URLS:
																					_a			  =       self.config_name.lower()

														elif self.config_name.upper() in CHECKPOINT_URLS:
																					_a			  =       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
														_a			  =       dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]      )
														_a			  =       score.BleurtScorer(os.path.join(lowerCAmelCase_							,    lowerCAmelCase_      )      )







							def      __lowerCAmelCase    (						self :						str							,    lowerCAmelCase_ :						int							,    lowerCAmelCase_ :						Union[str, Any]      )    ->							Optional[int]:


														"""simple docstring"""


														_a			  =       self.scorer.score(references=lowerCAmelCase_							,    candidates=lowerCAmelCase_      )
														return {"scores": scores}

