






import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

SCREAMING_SNAKE_CASE__				     =			3

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

def        UpperCAmelCase__		(						lowerCamelCase_       :       int				):
			print('Generating prime p...'				)
			__a						:  str									=     rabin_miller.generate_large_prime(lowerCamelCase_				)  # select large prime number.
			__a						:  List[Any]									=     primitive_root(lowerCamelCase_				)  # one primitive root on modulo p.
			__a						:  Tuple									=     random.randrange(3   ,  lowerCamelCase_				)  # private_key -> have to be greater than 2 for safety.
			__a						:  int									=     cryptomath.find_mod_inverse(pow(lowerCamelCase_   ,  lowerCamelCase_   ,  lowerCamelCase_				)   ,  lowerCamelCase_				)

			__a						:  List[str]									=     (key_size, e_a, e_a, p)
			__a						:  List[Any]									=     (key_size, d)

			return public_key, private_key

def        UpperCAmelCase__		(						lowerCamelCase_       :       str   ,  lowerCamelCase_       :       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()

			__a						,					__a						:  Dict									=     generate_key(lowerCamelCase_				)
			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        UpperCAmelCase__		(						):
			print('Making key files...'				)
			make_key_files('elgamal'   ,  2_0_4_8				)
			print('Key files generation successful'				)


if __name__ == "__main__":
							main()










"""simple docstring"""






import inspect

import jax
import jax.lax as lax
import jax.numpy as jnp

from ..utils import add_start_docstrings
from ..utils.logging import get_logger


__snake_case				:  List[str] =       get_logger(__name__)


__snake_case				:  str =       R'\n    Args:\n        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam\n            search or log softmax for each vocabulary token when using beam search\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional logits processor specific kwargs.\n\n    Return:\n        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.\n\n'



class      A__  :




	'''simple docstring'''





	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray)							->			jnp.ndarray:

				"""simple docstring"""

				raise NotImplementedError(
				    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""")






class      A__  :




	'''simple docstring'''





	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray)							->			jnp.ndarray:

				"""simple docstring"""

				raise NotImplementedError(
				    F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""")






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@add_start_docstrings(_SCREAMING_SNAKE_CASE)
	def __call__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int			,     **_SCREAMING_SNAKE_CASE:						str)							->			jnp.ndarray:

				"""simple docstring"""

				for processor in self:
							__lowerCAmelCase :    int				=			inspect.signature(processor.__call__).parameters
							if len(_SCREAMING_SNAKE_CASE) > 3:
										if not all(arg in kwargs for arg in list(function_args.keys())[2:]):
													raise ValueError(
													    F"""Make sure that all the required parameters: {list(function_args.keys())} for """
													    F"""{processor.__class__} are passed to the logits processor.""")
										__lowerCAmelCase :    int				=			processor(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     **_SCREAMING_SNAKE_CASE)
							else:
										__lowerCAmelCase :    Union[str, Any]				=			processor(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						int			,     _SCREAMING_SNAKE_CASE:						float)							->			str:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE) or not (temperature > 0):
							raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}""")

				__lowerCAmelCase :    List[str]				=			temperature







	def __call__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			scores / self.temperature
				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Dict			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						float = -float("Inf")			,     _SCREAMING_SNAKE_CASE:						int = 1)							->			Optional[int]:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE) or (top_p < 0 or top_p > 1.0):
							raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}""")
				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE) or (min_tokens_to_keep < 1):
							raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}""")

				__lowerCAmelCase :    Union[str, Any]				=			top_p
				__lowerCAmelCase :    Optional[int]				=			filter_value
				__lowerCAmelCase :    str				=			min_tokens_to_keep







	def __call__(	self:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Optional[Any]				=			lax.top_k(_SCREAMING_SNAKE_CASE			,     scores.shape[-1])

				__lowerCAmelCase :    List[str]				=			jnp.full_like(_SCREAMING_SNAKE_CASE			,     self.filter_value)
				__lowerCAmelCase :    Any				=			jax.nn.softmax(_SCREAMING_SNAKE_CASE			,     axis=-1).cumsum(axis=-1)
				__lowerCAmelCase :    Optional[Any]				=			cumulative_probs < self.top_p

				# include the token that is higher than top_p as well
				__lowerCAmelCase :    Tuple				=			jnp.roll(_SCREAMING_SNAKE_CASE			,     1)
				score_mask |= score_mask.at[:, 0].set(_SCREAMING_SNAKE_CASE)

				# min tokens to keep
				__lowerCAmelCase :    Union[str, Any]				=			score_mask.at[:, : self.min_tokens_to_keep].set(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			jnp.where(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Dict				=			jax.lax.sort_key_val(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)[-1]

				return next_scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						float = -float("Inf")			,     _SCREAMING_SNAKE_CASE:						int = 1)							->			Optional[int]:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE) or top_k <= 0:
							raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}""")

				__lowerCAmelCase :    Any				=			max(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[int]				=			filter_value







	def __call__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Any				=			scores.shape
				__lowerCAmelCase :    Union[str, Any]				=			jnp.full(batch_size * vocab_size			,     self.filter_value)

				__lowerCAmelCase :    List[str]				=			min(self.top_k			,     scores.shape[-1])  # Safety check
				__lowerCAmelCase		,      __lowerCAmelCase :    List[str]				=			lax.top_k(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    List[str]				=			jnp.broadcast_to((jnp.arange(_SCREAMING_SNAKE_CASE) * vocab_size)[:, None]			,     (batch_size, topk)).flatten()
				__lowerCAmelCase :    Any				=			topk_scores.flatten()
				__lowerCAmelCase :    Any				=			topk_indices.flatten() + shift

				__lowerCAmelCase :    str				=			next_scores_flat.at[topk_indices_flat].set(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Union[str, Any]				=			next_scores_flat.reshape(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				return next_scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Tuple				=			bos_token_id







	def __call__(	self:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			jnp.full(scores.shape			,     -float("inf"))

				__lowerCAmelCase :    int				=			1 - jnp.bool_(cur_len - 1)

				__lowerCAmelCase :    Tuple				=			jnp.where(_SCREAMING_SNAKE_CASE			,     new_scores.at[:, self.bos_token_id].set(0)			,     _SCREAMING_SNAKE_CASE)

				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			max_length
				__lowerCAmelCase :    int				=			eos_token_id







	def __call__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			jnp.full(scores.shape			,     -float("inf"))

				__lowerCAmelCase :    Any				=			1 - jnp.bool_(cur_len - self.max_length + 1)

				__lowerCAmelCase :    Union[str, Any]				=			jnp.where(_SCREAMING_SNAKE_CASE			,     new_scores.at[:, self.eos_token_id].set(0)			,     _SCREAMING_SNAKE_CASE)

				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int)							->			Optional[Any]:

				"""simple docstring"""

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE) or min_length < 0:
							raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}""")

				if not isinstance(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE) or eos_token_id < 0:
							raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}""")

				__lowerCAmelCase :    Any				=			min_length
				__lowerCAmelCase :    Tuple				=			eos_token_id







	def __call__(	self:						str			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			1 - jnp.clip(cur_len - self.min_length			,     0			,     1)

				__lowerCAmelCase :    Union[str, Any]				=			jnp.where(_SCREAMING_SNAKE_CASE			,     scores.at[:, self.eos_token_id].set(-float("inf"))			,     _SCREAMING_SNAKE_CASE)

				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						str)							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    int				=			list(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Optional[Any]				=			begin_index







	def __call__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						int)							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			1 - jnp.bool_(cur_len - self.begin_index)

				__lowerCAmelCase :    Optional[Any]				=			jnp.where(_SCREAMING_SNAKE_CASE			,     scores.at[:, self.begin_suppress_tokens].set(-float("inf"))			,     _SCREAMING_SNAKE_CASE)

				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						list)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			list(_SCREAMING_SNAKE_CASE)







	def __call__(	self:						int			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			scores.at[..., self.suppress_tokens].set(-float("inf"))

				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			dict(_SCREAMING_SNAKE_CASE)
				# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
				# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
				# Indexes without forced tokens will have a negative value.
				__lowerCAmelCase :    Any				=			jnp.ones((max(force_token_map.keys()) + 1)			,     dtype=jnp.intaa) * -1
				for index, token in force_token_map.items():
							if token is not None:
										__lowerCAmelCase :    int				=			force_token_array.at[index].set(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Tuple				=			jnp.intaa(_SCREAMING_SNAKE_CASE)







	def __call__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						jnp.ndarray			,     _SCREAMING_SNAKE_CASE:						int)							->			jnp.ndarray:

				"""simple docstring"""

				def _force_token(_SCREAMING_SNAKE_CASE:						Optional[Any]):
							__lowerCAmelCase :    int				=			scores.shape[0]
							__lowerCAmelCase :    Optional[int]				=			self.force_token_array[generation_idx]

							__lowerCAmelCase :    int				=			jnp.ones_like(_SCREAMING_SNAKE_CASE			,     dtype=scores.dtype) * -float("inf")
							__lowerCAmelCase :    Optional[int]				=			jnp.zeros((batch_size, 1)			,     dtype=scores.dtype)
							__lowerCAmelCase :    Dict				=			lax.dynamic_update_slice(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     (0, current_token))
							return new_scores

				__lowerCAmelCase :    Union[str, Any]				=			lax.cond(
				    cur_len >= self.force_token_array.shape[0]			,     lambda: scores			,     lambda: lax.cond(
				        self.force_token_array[cur_len] >= 0			,     lambda: _force_token(_SCREAMING_SNAKE_CASE)			,     lambda: scores			,     )			,     )
				return scores






class      A__  (    __SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	def __init__(	self:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						Optional[Any])							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    List[str]				=			generate_config.eos_token_id
				__lowerCAmelCase :    Optional[int]				=			generate_config.no_timestamps_token_id
				__lowerCAmelCase :    Dict				=			generate_config.no_timestamps_token_id + 1

				__lowerCAmelCase :    Optional[Any]				=			decoder_input_length + 1

				if generate_config.is_multilingual:
							# room for language token and task token
							self.begin_index += 2
				if hasattr(_SCREAMING_SNAKE_CASE			,     "max_initial_timestamp_index"):
							__lowerCAmelCase :    Optional[int]				=			generate_config.max_initial_timestamp_index
				else:
							__lowerCAmelCase :    Optional[int]				=			model_config.vocab_size
				if self.max_initial_timestamp_index is None:
							__lowerCAmelCase :    int				=			model_config.vocab_size







	def __call__(	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]			,     _SCREAMING_SNAKE_CASE:						Optional[Any]			,     _SCREAMING_SNAKE_CASE:						int)							->			Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[Any]				=			scores.at[:, self.no_timestamps_token_id].set(-float("inf"))

				def handle_pairs(_SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]):
							__lowerCAmelCase :    Tuple				=			jnp.where((cur_len - self.begin_index) >= 1			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    List[Any]				=			jnp.where(
							    input_ids_k[cur_len - 1] >= self.timestamp_begin			,     True and last_was_timestamp			,     _SCREAMING_SNAKE_CASE			,     )

							__lowerCAmelCase :    Tuple				=			jnp.where((cur_len - self.begin_index) < 2			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase :    int				=			jnp.where(
							    input_ids_k[cur_len - 2] >= self.timestamp_begin			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     )

							return jnp.where(
							    _SCREAMING_SNAKE_CASE			,     jnp.where(
							        penultimate_was_timestamp > 0			,     scores_k.at[self.timestamp_begin :].set(-float("inf"))			,     scores_k.at[: self.eos_token_id].set(-float("inf"))			,     )			,     _SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    int				=			jax.vmap(_SCREAMING_SNAKE_CASE)(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[str]				=			jnp.where(cur_len == self.begin_index			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			jnp.where(
				    self.max_initial_timestamp_index is not None			,     True and apply_max_initial_timestamp			,     _SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    List[Any]				=			self.timestamp_begin + self.max_initial_timestamp_index

				__lowerCAmelCase :    Tuple				=			jnp.where(
				    _SCREAMING_SNAKE_CASE			,     scores.at[:, last_allowed + 1 :].set(-float("inf"))			,     _SCREAMING_SNAKE_CASE			,     )

				# if sum of probability over timestamps is above any other token, sample timestamp
				__lowerCAmelCase :    List[str]				=			jax.nn.log_softmax(_SCREAMING_SNAKE_CASE			,     axis=-1)

				def handle_cumulative_probs(_SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						Any):
							__lowerCAmelCase :    Tuple				=			jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]			,     axis=-1)
							__lowerCAmelCase :    int				=			jnp.max(logprobs_k[: self.timestamp_begin])
							return jnp.where(
							    timestamp_logprob > max_text_token_logprob			,     scores_k.at[: self.timestamp_begin].set(-float("inf"))			,     _SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    Dict				=			jax.vmap(_SCREAMING_SNAKE_CASE)(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				return scores