






'''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


__lowerCAmelCase									=   get_logger(__name__)


__lowerCAmelCase									=   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 						SCREAMING_SNAKE_CASE       :






			@add_start_docstrings(__SCREAMING_SNAKE_CASE       )
			def __call__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray       )      ->      jnp.ndarray:
							raise NotImplementedError(
							    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'       )





class 						SCREAMING_SNAKE_CASE       :






			@add_start_docstrings(__SCREAMING_SNAKE_CASE       )
			def __call__(			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray       )      ->      jnp.ndarray:
							raise NotImplementedError(
							    f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'       )





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			@add_start_docstrings(__SCREAMING_SNAKE_CASE       )
			def __call__(			self   :      str	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      int	,    **__SCREAMING_SNAKE_CASE   :      str       )      ->      jnp.ndarray:
							for processor in self:
											a_		:	List[str]									=   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.'       )
															a_		:	List[Any]									=   processor(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )
											else:
															a_		:	str									=   processor(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							return scores





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      float       )      ->      Optional[Any]:
							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}'       )

							a_		:	Optional[Any]									=   temperature







			def __call__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      jnp.ndarray:
							a_		:	Any									=   scores / self.temperature
							return scores





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      float	,    __SCREAMING_SNAKE_CASE   :      float = -float('''Inf'''       )	,    __SCREAMING_SNAKE_CASE   :      int = 1       )      ->      List[Any]:
							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}'       )

							a_		:	Union[str, Any]									=   top_p
							a_		:	Dict									=   filter_value
							a_		:	Dict									=   min_tokens_to_keep







			def __call__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      jnp.ndarray:
							a_					,      a_		:	Optional[Any]									=   lax.top_k(__SCREAMING_SNAKE_CASE	,    scores.shape[-1]       )

							a_		:	Dict									=   jnp.full_like(__SCREAMING_SNAKE_CASE	,    self.filter_value       )
							a_		:	Dict									=   jax.nn.softmax(__SCREAMING_SNAKE_CASE	,    axis=-1       ).cumsum(axis=-1       )
							a_		:	Union[str, Any]									=   cumulative_probs < self.top_p

							# include the token that is higher than top_p as well
							a_		:	List[Any]									=   jnp.roll(__SCREAMING_SNAKE_CASE	,    1       )
							score_mask |= score_mask.at[:, 0].set(__SCREAMING_SNAKE_CASE       )

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

							a_		:	str									=   jnp.where(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   jax.lax.sort_key_val(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )[-1]

							return next_scores





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      float = -float('''Inf'''       )	,    __SCREAMING_SNAKE_CASE   :      int = 1       )      ->      Dict:
							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}'       )

							a_		:	List[str]									=   max(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							a_		:	Union[str, Any]									=   filter_value







			def __call__(			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      jnp.ndarray:
							a_					,      a_		:	Optional[int]									=   scores.shape
							a_		:	Any									=   jnp.full(batch_size * vocab_size	,    self.filter_value       )

							a_		:	str									=   min(self.top_k	,    scores.shape[-1]       )  # Safety check
							a_					,      a_		:	Optional[int]									=   lax.top_k(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							a_		:	Optional[Any]									=   jnp.broadcast_to((jnp.arange(__SCREAMING_SNAKE_CASE       ) * vocab_size)[:, None]	,    (batch_size, topk)       ).flatten()
							a_		:	Tuple									=   topk_scores.flatten()
							a_		:	Union[str, Any]									=   topk_indices.flatten() + shift

							a_		:	List[Any]									=   next_scores_flat.at[topk_indices_flat].set(__SCREAMING_SNAKE_CASE       )
							a_		:	int									=   next_scores_flat.reshape(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							return next_scores





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      str:
							a_		:	Tuple									=   bos_token_id







			def __call__(			self   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      jnp.ndarray:
							a_		:	Dict									=   jnp.full(scores.shape	,    -float('''inf'''       )       )

							a_		:	Union[str, Any]									=   1 - jnp.bool_(cur_len - 1       )

							a_		:	List[str]									=   jnp.where(__SCREAMING_SNAKE_CASE	,    new_scores.at[:, self.bos_token_id].set(0       )	,    __SCREAMING_SNAKE_CASE       )

							return scores





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      int:
							a_		:	Any									=   max_length
							a_		:	Union[str, Any]									=   eos_token_id







			def __call__(			self   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      jnp.ndarray:
							a_		:	str									=   jnp.full(scores.shape	,    -float('''inf'''       )       )

							a_		:	Optional[Any]									=   1 - jnp.bool_(cur_len - self.max_length + 1       )

							a_		:	Tuple									=   jnp.where(__SCREAMING_SNAKE_CASE	,    new_scores.at[:, self.eos_token_id].set(0       )	,    __SCREAMING_SNAKE_CASE       )

							return scores





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      int	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      List[str]:
							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}'       )

							a_		:	Optional[int]									=   min_length
							a_		:	Any									=   eos_token_id







			def __call__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      jnp.ndarray:
							# create boolean flag to decide if min length penalty should be applied
							a_		:	Optional[int]									=   1 - jnp.clip(cur_len - self.min_length	,    0	,    1       )

							a_		:	Optional[Any]									=   jnp.where(__SCREAMING_SNAKE_CASE	,    scores.at[:, self.eos_token_id].set(-float('''inf'''       )       )	,    __SCREAMING_SNAKE_CASE       )

							return scores





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      Optional[int]	,    __SCREAMING_SNAKE_CASE   :      List[Any]       )      ->      Optional[int]:
							a_		:	Union[str, Any]									=   list(__SCREAMING_SNAKE_CASE       )
							a_		:	Any									=   begin_index







			def __call__(			self   :      List[Any]	,    __SCREAMING_SNAKE_CASE   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      str	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      Dict:
							a_		:	str									=   1 - jnp.bool_(cur_len - self.begin_index       )

							a_		:	Tuple									=   jnp.where(__SCREAMING_SNAKE_CASE	,    scores.at[:, self.begin_suppress_tokens].set(-float('''inf'''       )       )	,    __SCREAMING_SNAKE_CASE       )

							return scores





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      list       )      ->      Optional[int]:
							a_		:	Any									=   list(__SCREAMING_SNAKE_CASE       )







			def __call__(			self   :      List[str]	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      jnp.ndarray:
							a_		:	Dict									=   scores.at[..., self.suppress_tokens].set(-float('''inf'''       )       )

							return scores





class 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      Dict	,    __SCREAMING_SNAKE_CASE   :      List[str]       )      ->      Union[str, Any]:
							a_		:	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.
							a_		:	Optional[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:
															a_		:	List[str]									=   force_token_array.at[index].set(__SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   jnp.intaa(__SCREAMING_SNAKE_CASE       )







			def __call__(			self   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      jnp.ndarray	,    __SCREAMING_SNAKE_CASE   :      int       )      ->      jnp.ndarray:
							def _force_token(__SCREAMING_SNAKE_CASE   :      Tuple       ):
											a_		:	Dict									=   scores.shape[0]
											a_		:	Tuple									=   self.force_token_array[generation_idx]

											a_		:	Any									=   jnp.ones_like(__SCREAMING_SNAKE_CASE	,    dtype=scores.dtype       ) * -float('''inf'''       )
											a_		:	List[str]									=   jnp.zeros((batch_size, 1)	,    dtype=scores.dtype       )
											a_		:	List[Any]									=   lax.dynamic_update_slice(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    (0, current_token)       )
											return new_scores

							a_		:	Dict									=   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 						SCREAMING_SNAKE_CASE       (       SCREAMING_SNAKE_CASE_							):






			def __init__(			self   :      Any	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Any       )      ->      Union[str, Any]:
							a_		:	Optional[Any]									=   generate_config.eos_token_id
							a_		:	List[str]									=   generate_config.no_timestamps_token_id
							a_		:	Tuple									=   generate_config.no_timestamps_token_id + 1

							a_		:	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'''       ):
											a_		:	Optional[int]									=   generate_config.max_initial_timestamp_index
							else:
											a_		:	Dict									=   model_config.vocab_size
							if self.max_initial_timestamp_index is None:
											a_		:	Tuple									=   model_config.vocab_size







			def __call__(			self   :      str	,    __SCREAMING_SNAKE_CASE   :      Any	,    __SCREAMING_SNAKE_CASE   :      Tuple	,    __SCREAMING_SNAKE_CASE   :      List[str]       )      ->      str:
							# suppress <|notimestamps|> which is handled by without_timestamps
							a_		:	Tuple									=   scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''       )       )

							def handle_pairs(__SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Optional[Any]       ):
											a_		:	Dict									=   jnp.where((cur_len - self.begin_index) >= 1	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
											a_		:	str									=   jnp.where(
											    input_ids_k[cur_len - 1] >= self.timestamp_begin	,    True and last_was_timestamp	,    __SCREAMING_SNAKE_CASE	,    )

											a_		:	Optional[Any]									=   jnp.where((cur_len - self.begin_index) < 2	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
											a_		:	Tuple									=   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	,    )

							a_		:	Union[str, Any]									=   jax.vmap(__SCREAMING_SNAKE_CASE       )(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

							a_		:	Dict									=   jnp.where(cur_len == self.begin_index	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
							a_		:	Tuple									=   jnp.where(
							    self.max_initial_timestamp_index is not None	,    True and apply_max_initial_timestamp	,    __SCREAMING_SNAKE_CASE	,    )

							a_		:	List[str]									=   self.timestamp_begin + self.max_initial_timestamp_index

							a_		:	List[Any]									=   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
							a_		:	Optional[Any]									=   jax.nn.log_softmax(__SCREAMING_SNAKE_CASE	,    axis=-1       )

							def handle_cumulative_probs(__SCREAMING_SNAKE_CASE   :      Union[str, Any]	,    __SCREAMING_SNAKE_CASE   :      Union[str, Any]       ):
											a_		:	Any									=   jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,    axis=-1       )
											a_		:	Optional[Any]									=   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	,    )

							a_		:	int									=   jax.vmap(__SCREAMING_SNAKE_CASE       )(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )

							return scores












'''simple docstring'''







def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	Optional[Any]									=   []
				a_		:	Optional[Any]									=   []
				a_		:	List[str]									=   {
				    '''^''': 3,
				    '''*''': 2,
				    '''/''': 2,
				    '''%''': 2,
				    '''+''': 1,
				    '''-''': 1,
				}  # Priority of each operator
				a_		:	int									=   len(__A   ) if (len(__A   ) > 7) else 7

				# Print table header for output
				print(
				    '''Symbol'''.center(8   )    ,		'''Stack'''.center(__A   )    ,		'''Postfix'''.center(__A   )    ,		sep=''' | '''    ,		)
				print('''-''' * (print_width * 3 + 7)   )

				for x in infix:
								if x.isalpha() or x.isdigit():
												post_fix.append(__A   )  # if x is Alphabet / Digit, add it to Postfix
								elif x == "(":
												stack.append(__A   )  # if x is "(" push to Stack
								elif x == ")":  # if x is ")" pop stack until "(" is encountered
												while stack[-1] != "(":
																post_fix.append(stack.pop()   )  # Pop stack & add the content to Postfix
												stack.pop()
								else:
												if len(__A   ) == 0:
																stack.append(__A   )  # If stack is empty, push x to stack
												else:  # while priority of x is not > priority of element in the stack
																while len(__A   ) > 0 and priority[x] <= priority[stack[-1]]:
																				post_fix.append(stack.pop()   )  # pop stack & add to Postfix
																stack.append(__A   )  # push x to stack

								print(
								    x.center(8   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		sep=''' | '''    ,		)  # Output in tabular format

				while len(__A   ) > 0:  # while stack is not empty
								post_fix.append(stack.pop()   )  # pop stack & add to Postfix
								print(
								    ''' '''.center(8   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		(''''''.join(__A   )).ljust(__A   )    ,		sep=''' | '''    ,		)  # Output in tabular format

				return "".join(__A   )  # return Postfix as str





def 		_UpperCAmelCase (				__A				: Tuple   ):
				a_		:	Union[str, Any]									=   list(infix[::-1]   )  # reverse the infix equation

				for i in range(len(__A   )   ):
								if infix[i] == "(":
												a_		:	List[str]									=   ''')'''  # change "(" to ")"
								elif infix[i] == ")":
												a_		:	str									=   '''('''  # change ")" to "("

				return (infix_2_postfix(''''''.join(__A   )   ))[
				    ::-1
				]  # call infix_2_postfix on Infix, return reverse of Postfix


if __name__ == "__main__":
		__lowerCAmelCase									=   input('\nEnter an Infix Equation = ')  # Input an Infix equation
		__lowerCAmelCase									=   ''.join(Infix.split())  # Remove spaces from the input
		print('\n\t', Infix, '(Infix) -> ', infix_2_prefix(Infix), '(Prefix)')




