





def    __UpperCamelCase      (    a,     a,     a=False) ->Dict:

					if isinstance(a,     a) and isinstance(a,     a):
										lowerCamelCase__       =       len(set_a.intersection(a))

										if alternative_union:
															lowerCamelCase__       =       len(a) + len(a)
										else:
															lowerCamelCase__       =       len(set_a.union(a))

										return intersection / union

					if isinstance(a,     (list, tuple)) and isinstance(a,     (list, tuple)):
										lowerCamelCase__       =       [element for element in set_a if element in set_b]

										if alternative_union:
															lowerCamelCase__       =       len(a) + len(a)
															return len(a) / union
										else:
															lowerCamelCase__       =       set_a + [element for element in set_b if element not in set_a]
															return len(a) / len(a)

										return len(a) / len(a)
					return None


if __name__ == "__main__":
						A_												=     {"a", "b", "c", "d", "e"}
						A_												=     {"c", "d", "e", "f", "h", "i"}
						print(jaccard_similarity(set_a, set_b))








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


A_												=     get_logger(__name__)


A_												=     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_ :





					"""simple docstring"""


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

class SCREAMING_SNAKE_CASE_ :





					"""simple docstring"""


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

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					@add_start_docstrings(_lowerCAmelCase	)
					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  **_lowerCAmelCase	):
										for processor in self:
															lowerCamelCase__       =       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."	)
																				lowerCamelCase__       =       processor(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	,  **_lowerCAmelCase	)
															else:
																				lowerCamelCase__       =       processor(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


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

										lowerCamelCase__       =       temperature






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       scores / self.temperature
										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase = -float("Inf"	)	,  _lowerCAmelCase = 1	):
										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}"	)

										lowerCamelCase__       =       top_p
										lowerCamelCase__       =       filter_value
										lowerCamelCase__       =       min_tokens_to_keep






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__    ,		lowerCamelCase__       =       lax.top_k(_lowerCAmelCase	,  scores.shape[-1]	)

										lowerCamelCase__       =       jnp.full_like(_lowerCAmelCase	,  self.filter_value	)
										lowerCamelCase__       =       jax.nn.softmax(_lowerCAmelCase	,  axis=-1	).cumsum(axis=-1	)
										lowerCamelCase__       =       cumulative_probs < self.top_p

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

										# min tokens to keep
										lowerCamelCase__       =       score_mask.at[:, : self.min_tokens_to_keep].set(_lowerCAmelCase	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       jax.lax.sort_key_val(_lowerCAmelCase	,  _lowerCAmelCase	)[-1]

										return next_scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase = -float("Inf"	)	,  _lowerCAmelCase = 1	):
										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}"	)

										lowerCamelCase__       =       max(_lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       filter_value






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__    ,		lowerCamelCase__       =       scores.shape
										lowerCamelCase__       =       jnp.full(batch_size * vocab_size	,  self.filter_value	)

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

										lowerCamelCase__       =       next_scores_flat.at[topk_indices_flat].set(_lowerCAmelCase	)
										lowerCamelCase__       =       next_scores_flat.reshape(_lowerCAmelCase	,  _lowerCAmelCase	)
										return next_scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       bos_token_id






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       jnp.full(scores.shape	,  -float("inf"	)	)

										lowerCamelCase__       =       1 - jnp.bool_(cur_len - 1	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  new_scores.at[:, self.bos_token_id].set(0	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       max_length
										lowerCamelCase__       =       eos_token_id






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       jnp.full(scores.shape	,  -float("inf"	)	)

										lowerCamelCase__       =       1 - jnp.bool_(cur_len - self.max_length + 1	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  new_scores.at[:, self.eos_token_id].set(0	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										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}"	)

										lowerCamelCase__       =       min_length
										lowerCamelCase__       =       eos_token_id






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

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  scores.at[:, self.eos_token_id].set(-float("inf"	)	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       list(_lowerCAmelCase	)
										lowerCamelCase__       =       begin_index






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       1 - jnp.bool_(cur_len - self.begin_index	)

										lowerCamelCase__       =       jnp.where(_lowerCAmelCase	,  scores.at[:, self.begin_suppress_tokens].set(-float("inf"	)	)	,  _lowerCAmelCase	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       list(_lowerCAmelCase	)






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       scores.at[..., self.suppress_tokens].set(-float("inf"	)	)

										return scores

class SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	):
										lowerCamelCase__       =       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.
										lowerCamelCase__       =       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__       =       force_token_array.at[index].set(_lowerCAmelCase	)
										lowerCamelCase__       =       jnp.intaa(_lowerCAmelCase	)






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										def _force_token(_lowerCAmelCase	):
															lowerCamelCase__       =       scores.shape[0]
															lowerCamelCase__       =       self.force_token_array[generation_idx]

															lowerCamelCase__       =       jnp.ones_like(_lowerCAmelCase	,  dtype=scores.dtype	) * -float("inf"	)
															lowerCamelCase__       =       jnp.zeros((batch_size, 1)	,  dtype=scores.dtype	)
															lowerCamelCase__       =       lax.dynamic_update_slice(_lowerCAmelCase	,  _lowerCAmelCase	,  (0, current_token)	)
															return new_scores

										lowerCamelCase__       =       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 SCREAMING_SNAKE_CASE_ (					lowercase_   ):





					"""simple docstring"""


					def __init__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										lowerCamelCase__       =       generate_config.eos_token_id
										lowerCamelCase__       =       generate_config.no_timestamps_token_id
										lowerCamelCase__       =       generate_config.no_timestamps_token_id + 1

										lowerCamelCase__       =       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"	):
															lowerCamelCase__       =       generate_config.max_initial_timestamp_index
										else:
															lowerCamelCase__       =       model_config.vocab_size
										if self.max_initial_timestamp_index is None:
															lowerCamelCase__       =       model_config.vocab_size






					def __call__( self	,  _lowerCAmelCase	,  _lowerCAmelCase	,  _lowerCAmelCase	):
										# suppress <|notimestamps|> which is handled by without_timestamps
										lowerCamelCase__       =       scores.at[:, self.no_timestamps_token_id].set(-float("inf"	)	)

										def handle_pairs(_lowerCAmelCase	,  _lowerCAmelCase	):
															lowerCamelCase__       =       jnp.where((cur_len - self.begin_index) >= 1	,  _lowerCAmelCase	,  _lowerCAmelCase	)
															lowerCamelCase__       =       jnp.where(
															    input_ids_k[cur_len - 1] >= self.timestamp_begin	,  True and last_was_timestamp	,  _lowerCAmelCase	,  )

															lowerCamelCase__       =       jnp.where((cur_len - self.begin_index) < 2	,  _lowerCAmelCase	,  _lowerCAmelCase	)
															lowerCamelCase__       =       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	,  )

										lowerCamelCase__       =       jax.vmap(_lowerCAmelCase	)(_lowerCAmelCase	,  _lowerCAmelCase	)

										lowerCamelCase__       =       jnp.where(cur_len == self.begin_index	,  _lowerCAmelCase	,  _lowerCAmelCase	)
										lowerCamelCase__       =       jnp.where(
										    self.max_initial_timestamp_index is not None	,  True and apply_max_initial_timestamp	,  _lowerCAmelCase	,  )

										lowerCamelCase__       =       self.timestamp_begin + self.max_initial_timestamp_index

										lowerCamelCase__       =       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
										lowerCamelCase__       =       jax.nn.log_softmax(_lowerCAmelCase	,  axis=-1	)

										def handle_cumulative_probs(_lowerCAmelCase	,  _lowerCAmelCase	):
															lowerCamelCase__       =       jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,  axis=-1	)
															lowerCamelCase__       =       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	,  )

										lowerCamelCase__       =       jax.vmap(_lowerCAmelCase	)(_lowerCAmelCase	,  _lowerCAmelCase	)

										return scores

