





"""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     :		List[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			:int     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray		):
												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			:Optional[Any]     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray		):
												raise NotImplementedError(
												    f"{self.__class__} is an abstract class. Only classes inheriting this class can be called."		)







class 					_UpperCAmelCase   (	lowercase_ ):


					@add_start_docstrings(__UpperCamelCase		)
					def __call__(					self			:str     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:int     ,       **__UpperCamelCase			:Any		):
												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   (	lowercase_ ):


					def __init__(					self			:Tuple     ,       __UpperCamelCase			:float		):
												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			:Optional[Any]     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:int		):
												A		=  scores / self.temperature
												return scores







class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:List[Any]     ,       __UpperCamelCase			:float     ,       __UpperCamelCase			:float = -float("Inf"		)     ,       __UpperCamelCase			:int = 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			:Dict     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:int		):
												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   (	lowercase_ ):


					def __init__(					self			:Any     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:float = -float("Inf"		)     ,       __UpperCamelCase			:int = 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			:Tuple     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:int		):
												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   (	lowercase_ ):


					def __init__(					self			:int     ,       __UpperCamelCase			:int		):
												A		=  bos_token_id


					def __call__(					self			:List[Any]     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:int		):
												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   (	lowercase_ ):


					def __init__(					self			:Tuple     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:int		):
												A		=  max_length
												A		=  eos_token_id


					def __call__(					self			:List[Any]     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:int		):
												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   (	lowercase_ ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:int     ,       __UpperCamelCase			:int		):
												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			:Any     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:int		):
												# 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   (	lowercase_ ):


					def __init__(					self			:Union[str, Any]     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Dict		):
												A		=  list(__UpperCamelCase		)
												A		=  begin_index


					def __call__(					self			:Optional[int]     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:List[str]     ,       __UpperCamelCase			:int		):
												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   (	lowercase_ ):


					def __init__(					self			:List[str]     ,       __UpperCamelCase			:list		):
												A		=  list(__UpperCamelCase		)


					def __call__(					self			:str     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:int		):
												A		=  scores.at[..., self.suppress_tokens].set(-float("inf"		)		)

												return scores







class 					_UpperCAmelCase   (	lowercase_ ):


					def __init__(					self			:Dict     ,       __UpperCamelCase			:List[str]		):
												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			:Tuple     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:jnp.ndarray     ,       __UpperCamelCase			:int		):
												def _force_token(__UpperCamelCase			:Any		):
																			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   (	lowercase_ ):


					def __init__(					self			:Optional[int]     ,       __UpperCamelCase			:Dict     ,       __UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:Optional[Any]		):
												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			:Any     ,       __UpperCamelCase			:str     ,       __UpperCamelCase			:Any     ,       __UpperCamelCase			:int		):
												# suppress <|notimestamps|> which is handled by without_timestamps
												A		=  scores.at[:, self.no_timestamps_token_id].set(-float("inf"		)		)

												def handle_pairs(__UpperCamelCase			:Union[str, Any]     ,       __UpperCamelCase			:List[Any]		):
																			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			:Dict     ,       __UpperCamelCase			:Optional[int]		):
																			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









"""simple docstring"""
# A Bipartite Graph is a graph whose vertices can be divided into two independent sets,
# U and V such that every edge (u, v) either connects a vertex from U to V or a vertex
# from V to U. In other words, for every edge (u, v), either u belongs to U and v to V,
# or u belongs to V and v to U. We can also say that there is no edge that connects
# vertices of same set.






def 					A__				(			UpperCamelCase       ):
							A		=  [False] * len(UpperCamelCase       )
							A		=  [-1] * len(UpperCamelCase       )

							def dfs(UpperCamelCase					,  UpperCamelCase       ):
														A		=  True
														A		=  c
														for u in graph[v]:
																					if not visited[u]:
																												dfs(UpperCamelCase					,  1 - c       )

							for i in range(len(UpperCamelCase       )       ):
														if not visited[i]:
																					dfs(UpperCamelCase					,  0       )

							for i in range(len(UpperCamelCase       )       ):
														for j in graph[i]:
																					if color[i] == color[j]:
																												return False

							return True


# Adjacency list of graph
_snake_case     :		str       						= {0: [1, 3], 1: [0, 2], 2: [1, 3], 3: [0, 2], 4: []}
print(check_bipartite_dfs(graph))


