


'''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    						=get_logger(__name__)


__snake_case    						=R"""
    Args:
        input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
            search or log softmax for each vocabulary token when using beam search
        kwargs (`Dict[str, Any]`, *optional*):
            Additional logits processor specific kwargs.

    Return:
        `jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.

"""

class 		UpperCAmelCase_      :





     @add_start_docstrings(UpperCAmelCase__    )
     def __call__( self       :      Dict    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      jnp.ndarray    )				->  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       :      str    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      jnp.ndarray    )				->  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       :      int    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      int    ,      **UpperCAmelCase__       :      Tuple    )				->  jnp.ndarray:
        for processor in self:
           lowerCAmelCase     =			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.'''    )
              lowerCAmelCase     =			processor(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    ,      **UpperCAmelCase__    )
           else:
              lowerCAmelCase     =			processor(UpperCAmelCase__    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
        return scores




class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      float    )				->  int:
        if not isinstance(UpperCAmelCase__    ,      UpperCAmelCase__    ) or not (temperature > 0):
           raise ValueError(F'''`temperature` has to be a strictly positive float, but is {temperature}'''    )

        lowerCAmelCase     =			temperature





     def __call__( self       :      Any    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      int    )				->  jnp.ndarray:
        lowerCAmelCase     =			scores / self.temperature
        return scores




class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      List[str]    ,      UpperCAmelCase__       :      float    ,      UpperCAmelCase__       :      float = -float('Inf'    )    ,      UpperCAmelCase__       :      int = 1    )				->  Optional[int]:
        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}'''    )

        lowerCAmelCase     =			top_p
        lowerCAmelCase     =			filter_value
        lowerCAmelCase     =			min_tokens_to_keep





     def __call__( self       :      str    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      int    )				->  jnp.ndarray:
        lowerCAmelCase     ,  lowerCAmelCase     =			lax.top_k(UpperCAmelCase__    ,      scores.shape[-1]    )

        lowerCAmelCase     =			jnp.full_like(UpperCAmelCase__    ,      self.filter_value    )
        lowerCAmelCase     =			jax.nn.softmax(UpperCAmelCase__    ,      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(UpperCAmelCase__    ,      1    )
        score_mask |= score_mask.at[:, 0].set(UpperCAmelCase__    )

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

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

        return next_scores




class 		UpperCAmelCase_      (  __lowercase		):





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

        lowerCAmelCase     =			max(UpperCAmelCase__    ,      UpperCAmelCase__    )
        lowerCAmelCase     =			filter_value





     def __call__( self       :      int    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      int    )				->  jnp.ndarray:
        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(UpperCAmelCase__    ,      UpperCAmelCase__    )
        lowerCAmelCase     =			jnp.broadcast_to((jnp.arange(UpperCAmelCase__    ) * 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(UpperCAmelCase__    )
        lowerCAmelCase     =			next_scores_flat.reshape(UpperCAmelCase__    ,      UpperCAmelCase__    )
        return next_scores




class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Optional[int]    ,      UpperCAmelCase__       :      int    )				->  str:
        lowerCAmelCase     =			bos_token_id





     def __call__( self       :      Dict    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      int    )				->  jnp.ndarray:
        lowerCAmelCase     =			jnp.full(scores.shape    ,      -float('inf'    )    )

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

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

        return scores




class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      int    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  List[Any]:
        lowerCAmelCase     =			max_length
        lowerCAmelCase     =			eos_token_id





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

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

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

        return scores




class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Optional[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  str:
        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}'''    )

        lowerCAmelCase     =			min_length
        lowerCAmelCase     =			eos_token_id





     def __call__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      int    )				->  jnp.ndarray:
        # 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(UpperCAmelCase__    ,      scores.at[:, self.eos_token_id].set(-float('inf'    )    )    ,      UpperCAmelCase__    )

        return scores




class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Tuple    ,      UpperCAmelCase__       :      Optional[int]    ,      UpperCAmelCase__       :      List[str]    )				->  List[str]:
        lowerCAmelCase     =			list(UpperCAmelCase__    )
        lowerCAmelCase     =			begin_index





     def __call__( self       :      Optional[Any]    ,      UpperCAmelCase__       :      List[str]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      int    )				->  List[Any]:
        lowerCAmelCase     =			1 - jnp.bool_(cur_len - self.begin_index    )

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

        return scores




class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      List[Any]    ,      UpperCAmelCase__       :      list    )				->  List[str]:
        lowerCAmelCase     =			list(UpperCAmelCase__    )





     def __call__( self       :      Optional[int]    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      int    )				->  jnp.ndarray:
        lowerCAmelCase     =			scores.at[..., self.suppress_tokens].set(-float('inf'    )    )

        return scores




class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Optional[Any]    ,      UpperCAmelCase__       :      List[Any]    )				->  List[str]:
        lowerCAmelCase     =			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.
        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(UpperCAmelCase__    )
        lowerCAmelCase     =			jnp.intaa(UpperCAmelCase__    )





     def __call__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      jnp.ndarray    ,      UpperCAmelCase__       :      int    )				->  jnp.ndarray:
        def _force_token(UpperCAmelCase__       :      Optional[int]    ):
           lowerCAmelCase     =			scores.shape[0]
           lowerCAmelCase     =			self.force_token_array[generation_idx]

           lowerCAmelCase     =			jnp.ones_like(UpperCAmelCase__    ,      dtype=scores.dtype    ) * -float('inf'    )
           lowerCAmelCase     =			jnp.zeros((batch_size, 1)    ,      dtype=scores.dtype    )
           lowerCAmelCase     =			lax.dynamic_update_slice(UpperCAmelCase__    ,      UpperCAmelCase__    ,      (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(UpperCAmelCase__    )    ,      lambda: scores    ,      )    ,      )
        return scores




class 		UpperCAmelCase_      (  __lowercase		):





     def __init__( self       :      Union[str, Any]    ,      UpperCAmelCase__       :      List[Any]    ,      UpperCAmelCase__       :      int    ,      UpperCAmelCase__       :      List[str]    )				->  List[str]:
        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(UpperCAmelCase__    ,      '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       :      List[Any]    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Tuple    ,      UpperCAmelCase__       :      Optional[int]    )				->  Union[str, Any]:
        # suppress <|notimestamps|> which is handled by without_timestamps
        lowerCAmelCase     =			scores.at[:, self.no_timestamps_token_id].set(-float('inf'    )    )

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

           lowerCAmelCase     =			jnp.where((cur_len - self.begin_index) < 2    ,      UpperCAmelCase__    ,      UpperCAmelCase__    )
           lowerCAmelCase     =			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__    ,      )

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

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

        lowerCAmelCase     =			self.timestamp_begin + self.max_initial_timestamp_index

        lowerCAmelCase     =			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
        lowerCAmelCase     =			jax.nn.log_softmax(UpperCAmelCase__    ,      axis=-1    )

        def handle_cumulative_probs(UpperCAmelCase__       :      Union[str, Any]    ,      UpperCAmelCase__       :      str    ):
           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'    )    )    ,      UpperCAmelCase__    ,      )

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

        return scores









'''simple docstring'''




def 	a_					( lowerCamelCase	:     Optional[Any]				):
   return [
       {
           0: [1, 2],
           1: [0, 2],
           2: [0, 1, 3, 5],
           3: [2, 4],
           4: [3],
           5: [2, 6, 8],
           6: [5, 7],
           7: [6, 8],
           8: [5, 7],
       },
       {
           0: [6],
           1: [9],
           2: [4, 5],
           3: [4],
           4: [2, 3],
           5: [2],
           6: [0, 7],
           7: [6],
           8: [],
           9: [1],
       },
       {
           0: [4],
           1: [6],
           2: [],
           3: [5, 6, 7],
           4: [0, 6],
           5: [3, 8, 9],
           6: [1, 3, 4, 7],
           7: [3, 6, 8, 9],
           8: [5, 7],
           9: [5, 7],
       },
       {
           0: [1, 3],
           1: [0, 2, 4],
           2: [1, 3, 4],
           3: [0, 2, 4],
           4: [1, 2, 3],
       },
   ][index]





def 	a_					( lowerCamelCase	:     dict[int, list[int]]				):
   lowerCAmelCase     =			0
   lowerCAmelCase     =			len(lowerCamelCase				)  # No of vertices in graph
   lowerCAmelCase     =			[0] * n
   lowerCAmelCase     =			[False] * n

   def dfs(lowerCamelCase	:     Tuple	, lowerCamelCase	:     str	, lowerCamelCase	:     Dict	, lowerCamelCase	:     str				):
      lowerCAmelCase     =			True
      lowerCAmelCase     =			id_
      id_ += 1
      for to in graph[at]:
         if to == parent:
            pass
         elif not visited[to]:
            dfs(lowerCamelCase	, lowerCamelCase	, lowerCamelCase	, id_				)
            lowerCAmelCase     =			min(low[at]	, low[to]				)
            if id_ <= low[to]:
               bridges.append((at, to) if at < to else (to, at)				)
         else:
            # This edge is a back edge and cannot be a bridge
            lowerCAmelCase     =			min(low[at]	, low[to]				)

   lowerCAmelCase     =			[]
   for i in range(lowerCamelCase				):
      if not visited[i]:
         dfs(lowerCamelCase	, -1	, lowerCamelCase	, id_				)
   return bridges


if __name__ == "__main__":
       import doctest

       doctest.testmod()





