



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_							:      Optional[Any]      =  get_logger(__name__)


lowerCamelCase_							:      Union[str, Any]      =  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      UpperCamelCase    :



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






class      UpperCamelCase    :



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






class      UpperCamelCase    (						_UpperCAmelCase     ):



	@add_start_docstrings(UpperCAmelCase__		)
	def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	**UpperCAmelCase__		):
							for processor in self:
													A__							=       inspect.signature(processor.__call__		).parameters
													if len(UpperCAmelCase__		) > 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__							=       processor(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__			,	**UpperCAmelCase__		)
													else:
																			A__							=       processor(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)
							return scores






class      UpperCamelCase    (						_UpperCAmelCase     ):



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

							A__							=       temperature



	def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       scores / self.temperature
							return scores






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__ = -float("Inf"		)			,	UpperCAmelCase__ = 1		):
							if not isinstance(UpperCAmelCase__			,	UpperCAmelCase__		) 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(UpperCAmelCase__			,	UpperCAmelCase__		) 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__							=       top_p
							A__							=       filter_value
							A__							=       min_tokens_to_keep



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

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

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

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

							A__							=       jnp.where(UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		)
							A__							=       jax.lax.sort_key_val(UpperCAmelCase__			,	UpperCAmelCase__		)[-1]

							return next_scores






class      UpperCamelCase    (						_UpperCAmelCase     ):



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

							A__							=       max(UpperCAmelCase__			,	UpperCAmelCase__		)
							A__							=       filter_value



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

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

							A__							=       next_scores_flat.at[topk_indices_flat].set(UpperCAmelCase__		)
							A__							=       next_scores_flat.reshape(UpperCAmelCase__			,	UpperCAmelCase__		)
							return next_scores






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__		):
							A__							=       bos_token_id



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

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

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

							return scores






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       max_length
							A__							=       eos_token_id



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

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

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

							return scores






class      UpperCamelCase    (						_UpperCAmelCase     ):



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

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

							A__							=       min_length
							A__							=       eos_token_id



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

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

							return scores






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       list(UpperCAmelCase__		)
							A__							=       begin_index



	def __call__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       1 - jnp.bool_(cur_len - self.begin_index		)

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

							return scores






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__		):
							A__							=       list(UpperCAmelCase__		)



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

							return scores






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__		):
							A__							=       dict(UpperCAmelCase__		)
							# 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__							=       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__							=       force_token_array.at[index].set(UpperCAmelCase__		)
							A__							=       jnp.intaa(UpperCAmelCase__		)



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

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

							A__							=       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(UpperCAmelCase__		)			,	lambda: scores			,	)			,	)
							return scores






class      UpperCamelCase    (						_UpperCAmelCase     ):



	def __init__(       self			,	UpperCAmelCase__			,	UpperCAmelCase__			,	UpperCAmelCase__		):
							A__							=       generate_config.eos_token_id
							A__							=       generate_config.no_timestamps_token_id
							A__							=       generate_config.no_timestamps_token_id + 1

							A__							=       decoder_input_length + 1

							if generate_config.is_multilingual:
													# room for language token and task token
													self.begin_index += 2
							if hasattr(UpperCAmelCase__			,	"max_initial_timestamp_index"		):
													A__							=       generate_config.max_initial_timestamp_index
							else:
													A__							=       model_config.vocab_size
							if self.max_initial_timestamp_index is None:
													A__							=       model_config.vocab_size



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

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

													A__							=       jnp.where((cur_len - self.begin_index) < 2			,	UpperCAmelCase__			,	UpperCAmelCase__		)
													A__							=       jnp.where(
													    input_ids_k[cur_len - 2] >= self.timestamp_begin			,	UpperCAmelCase__			,	UpperCAmelCase__			,	)

													return jnp.where(
													    UpperCAmelCase__			,	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"		)		)			,	)			,	UpperCAmelCase__			,	)

							A__							=       jax.vmap(UpperCAmelCase__		)(UpperCAmelCase__			,	UpperCAmelCase__		)

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

							A__							=       self.timestamp_begin + self.max_initial_timestamp_index

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

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

							def handle_cumulative_probs(UpperCAmelCase__			,	UpperCAmelCase__		):
													A__							=       jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]			,	axis=-1		)
													A__							=       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"		)		)			,	UpperCAmelCase__			,	)

							A__							=       jax.vmap(UpperCAmelCase__		)(UpperCAmelCase__			,	UpperCAmelCase__		)

							return scores










def  UpperCamelCase						(       _A			:				int			,							_A			:				int    )->       int:





						"""simple docstring"""





						return int((input_a, input_a).count(0    ) == 0    )
def  UpperCamelCase						(       )->       None:





						"""simple docstring"""





						assert and_gate(0			,							0    ) == 0
						assert and_gate(0			,							1    ) == 0
						assert and_gate(1			,							0    ) == 0
						assert and_gate(1			,							1    ) == 1


if __name__ == "__main__":
						test_and_gate()
						print(and_gate(1, 0))
						print(and_gate(0, 0))
						print(and_gate(0, 1))
						print(and_gate(1, 1))





