






"""simple docstring"""
def 				lowercase__		(					lowerCamelCase = 1_000       ):
				return sum(e for e in range(3,						lowerCamelCase       ) if e % 3 == 0 or e % 5 == 0       )


if __name__ == "__main__":
			print(F'{solution() = }')









"""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 _lowerCAmelCase						:







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







class _lowerCAmelCase						:







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







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						@add_start_docstrings(lowerCAmelCase_							)
						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							)		->							jnp.ndarray:
										for processor in self:
														_SCREAMING_SNAKE_CASE   :		int       		=    inspect.signature(processor.__call__							).parameters
														if len(lowerCAmelCase_							) > 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."""							)
																		_SCREAMING_SNAKE_CASE   :		Any       		=    processor(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				**lowerCAmelCase_							)
														else:
																		_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    processor(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_							)		->							Any:
										if not isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							) or not (temperature > 0):
														raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}"""							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    temperature







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    scores / self.temperature
										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = -float('Inf'							)   ,				lowerCAmelCase_ = 1							)		->							int:
										if not isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							) 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(lowerCAmelCase_   ,				lowerCAmelCase_							) 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}"""							)

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







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		Dict       		=    lax.top_k(lowerCAmelCase_   ,				scores.shape[-1]							)

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

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

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

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    jnp.where(lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Dict       		=    jax.lax.sort_key_val(lowerCAmelCase_   ,				lowerCAmelCase_							)[-1]

										return next_scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_ = -float('Inf'							)   ,				lowerCAmelCase_ = 1							)		->							Tuple:
										if not isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							) or top_k <= 0:
														raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}"""							)

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    max(lowerCAmelCase_   ,				lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    filter_value







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE   :		int       		=    scores.shape
										_SCREAMING_SNAKE_CASE   :		int       		=    jnp.full(batch_size * vocab_size   ,				self.filter_value							)

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

										_SCREAMING_SNAKE_CASE   :		List[str]       		=    next_scores_flat.at[topk_indices_flat].set(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		int       		=    next_scores_flat.reshape(lowerCAmelCase_   ,				lowerCAmelCase_							)
										return next_scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_							)		->							Dict:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    bos_token_id







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    jnp.full(scores.shape   ,				-float('inf'							)							)

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

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

										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							List[Any]:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    max_length
										_SCREAMING_SNAKE_CASE   :		Any       		=    eos_token_id







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.full(scores.shape   ,				-float('inf'							)							)

										_SCREAMING_SNAKE_CASE   :		Dict       		=    1 - jnp.bool_(cur_len - self.max_length + 1							)

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

										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Optional[int]:
										if not isinstance(lowerCAmelCase_   ,				lowerCAmelCase_							) or min_length < 0:
														raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}"""							)

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

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    min_length
										_SCREAMING_SNAKE_CASE   :		str       		=    eos_token_id







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										# create boolean flag to decide if min length penalty should be applied
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    1 - jnp.clip(cur_len - self.min_length   ,				0   ,				1							)

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

										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







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







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Any       		=    1 - jnp.bool_(cur_len - self.begin_index							)

										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    jnp.where(lowerCAmelCase_   ,				scores.at[:, self.begin_suppress_tokens].set(-float('inf'							)							)   ,				lowerCAmelCase_							)

										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_							)		->							Optional[Any]:
										_SCREAMING_SNAKE_CASE   :		str       		=    list(lowerCAmelCase_							)







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    scores.at[..., self.suppress_tokens].set(-float('inf'							)							)

										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_							)		->							Any:
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    dict(lowerCAmelCase_							)
										# 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.
										_SCREAMING_SNAKE_CASE   :		Dict       		=    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:
																		_SCREAMING_SNAKE_CASE   :		Dict       		=    force_token_array.at[index].set(lowerCAmelCase_							)
										_SCREAMING_SNAKE_CASE   :		str       		=    jnp.intaa(lowerCAmelCase_							)







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							jnp.ndarray:
										def _force_token(lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		str       		=    scores.shape[0]
														_SCREAMING_SNAKE_CASE   :		List[Any]       		=    self.force_token_array[generation_idx]

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

										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    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(lowerCAmelCase_							)   ,				lambda: scores   ,				)   ,				)
										return scores







class _lowerCAmelCase						(    __UpperCAmelCase       ):







						def __init__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							str:
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    generate_config.eos_token_id
										_SCREAMING_SNAKE_CASE   :		str       		=    generate_config.no_timestamps_token_id
										_SCREAMING_SNAKE_CASE   :		Optional[int]       		=    generate_config.no_timestamps_token_id + 1

										_SCREAMING_SNAKE_CASE   :		str       		=    decoder_input_length + 1

										if generate_config.is_multilingual:
														# room for language token and task token
														self.begin_index += 2
										if hasattr(lowerCAmelCase_   ,				'max_initial_timestamp_index'							):
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    generate_config.max_initial_timestamp_index
										else:
														_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    model_config.vocab_size
										if self.max_initial_timestamp_index is None:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    model_config.vocab_size







						def __call__(    self   ,				lowerCAmelCase_   ,				lowerCAmelCase_   ,				lowerCAmelCase_							)		->							Dict:
										# suppress <|notimestamps|> which is handled by without_timestamps
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    scores.at[:, self.no_timestamps_token_id].set(-float('inf'							)							)

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

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

														return jnp.where(
														    lowerCAmelCase_   ,				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'							)							)   ,				)   ,				lowerCAmelCase_   ,				)

										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    jax.vmap(lowerCAmelCase_							)(lowerCAmelCase_   ,				lowerCAmelCase_							)

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

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    self.timestamp_begin + self.max_initial_timestamp_index

										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    jnp.where(
										    lowerCAmelCase_   ,				scores.at[:, last_allowed + 1 :].set(-float('inf'							)							)   ,				lowerCAmelCase_   ,				)

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

										def handle_cumulative_probs(lowerCAmelCase_   ,				lowerCAmelCase_							):
														_SCREAMING_SNAKE_CASE   :		Any       		=    jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]   ,				axis=-1							)
														_SCREAMING_SNAKE_CASE   :		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'							)							)   ,				lowerCAmelCase_   ,				)

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

										return scores

