






'''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


UpperCamelCase      					=get_logger(__name__)


UpperCamelCase      					=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      A     :






 """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      A     :






 """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      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 @add_start_docstrings(__lowerCAmelCase			)
 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  **__lowerCAmelCase			):
       for processor in self:
             UpperCamelCase_					: List[str]            =				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."			)
                   UpperCamelCase_					: int            =				processor(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  **__lowerCAmelCase			)
             else:
                   UpperCamelCase_					: List[Any]            =				processor(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
       return scores






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """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}"			)

       UpperCamelCase_					: Union[str, Any]            =				temperature





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Optional[int]            =				scores / self.temperature
       return scores






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """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}"			)

       UpperCamelCase_					: Optional[int]            =				top_p
       UpperCamelCase_					: Dict            =				filter_value
       UpperCamelCase_					: Optional[int]            =				min_tokens_to_keep





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_     ,      UpperCamelCase_					: str            =				lax.top_k(__lowerCAmelCase      ,  scores.shape[-1]			)

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

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

       # min tokens to keep
       UpperCamelCase_					: List[Any]            =				score_mask.at[:, : self.min_tokens_to_keep].set(__lowerCAmelCase			)

       UpperCamelCase_					: Any            =				jnp.where(__lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
       UpperCamelCase_					: Optional[int]            =				jax.lax.sort_key_val(__lowerCAmelCase      ,  __lowerCAmelCase			)[-1]

       return next_scores






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """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}"			)

       UpperCamelCase_					: Any            =				max(__lowerCAmelCase      ,  __lowerCAmelCase			)
       UpperCamelCase_					: List[str]            =				filter_value





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_     ,      UpperCamelCase_					: Optional[Any]            =				scores.shape
       UpperCamelCase_					: List[Any]            =				jnp.full(batch_size * vocab_size      ,  self.filter_value			)

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

       UpperCamelCase_					: Tuple            =				next_scores_flat.at[topk_indices_flat].set(__lowerCAmelCase			)
       UpperCamelCase_					: str            =				next_scores_flat.reshape(__lowerCAmelCase      ,  __lowerCAmelCase			)
       return next_scores






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Union[str, Any]            =				bos_token_id





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Tuple            =				jnp.full(scores.shape      ,  -float("""inf"""			)			)

       UpperCamelCase_					: Any            =				1 - jnp.bool_(cur_len - 1			)

       UpperCamelCase_					: List[Any]            =				jnp.where(__lowerCAmelCase      ,  new_scores.at[:, self.bos_token_id].set(0			)      ,  __lowerCAmelCase			)

       return scores






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: int            =				max_length
       UpperCamelCase_					: Union[str, Any]            =				eos_token_id





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Optional[Any]            =				jnp.full(scores.shape      ,  -float("""inf"""			)			)

       UpperCamelCase_					: int            =				1 - jnp.bool_(cur_len - self.max_length + 1			)

       UpperCamelCase_					: Dict            =				jnp.where(__lowerCAmelCase      ,  new_scores.at[:, self.eos_token_id].set(0			)      ,  __lowerCAmelCase			)

       return scores






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """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}"			)

       UpperCamelCase_					: str            =				min_length
       UpperCamelCase_					: Optional[int]            =				eos_token_id





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       # create boolean flag to decide if min length penalty should be applied
       UpperCamelCase_					: Optional[Any]            =				1 - jnp.clip(cur_len - self.min_length      ,  0      ,  1			)

       UpperCamelCase_					: str            =				jnp.where(__lowerCAmelCase      ,  scores.at[:, self.eos_token_id].set(-float("""inf"""			)			)      ,  __lowerCAmelCase			)

       return scores






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Dict            =				list(__lowerCAmelCase			)
       UpperCamelCase_					: List[str]            =				begin_index





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Any            =				1 - jnp.bool_(cur_len - self.begin_index			)

       UpperCamelCase_					: Any            =				jnp.where(__lowerCAmelCase      ,  scores.at[:, self.begin_suppress_tokens].set(-float("""inf"""			)			)      ,  __lowerCAmelCase			)

       return scores






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Optional[Any]            =				list(__lowerCAmelCase			)





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: List[str]            =				scores.at[..., self.suppress_tokens].set(-float("""inf"""			)			)

       return scores






class      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase			):
       UpperCamelCase_					: List[str]            =				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.
       UpperCamelCase_					: 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:
                   UpperCamelCase_					: Tuple            =				force_token_array.at[index].set(__lowerCAmelCase			)
       UpperCamelCase_					: Tuple            =				jnp.intaa(__lowerCAmelCase			)





 def __call__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       def _force_token(__lowerCAmelCase			):
             UpperCamelCase_					: str            =				scores.shape[0]
             UpperCamelCase_					: str            =				self.force_token_array[generation_idx]

             UpperCamelCase_					: Any            =				jnp.ones_like(__lowerCAmelCase      ,  dtype=scores.dtype			) * -float("""inf"""			)
             UpperCamelCase_					: Optional[int]            =				jnp.zeros((batch_size, 1)      ,  dtype=scores.dtype			)
             UpperCamelCase_					: List[str]            =				lax.dynamic_update_slice(__lowerCAmelCase      ,  __lowerCAmelCase      ,  (0, current_token)			)
             return new_scores

       UpperCamelCase_					: 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      A     (   SCREAMING_SNAKE_CASE__    ):






 """simple docstring"""





 def __init__(				self      ,  __lowerCAmelCase      ,  __lowerCAmelCase      ,  __lowerCAmelCase			):
       UpperCamelCase_					: Optional[Any]            =				generate_config.eos_token_id
       UpperCamelCase_					: List[str]            =				generate_config.no_timestamps_token_id
       UpperCamelCase_					: List[Any]            =				generate_config.no_timestamps_token_id + 1

       UpperCamelCase_					: 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"""			):
             UpperCamelCase_					: Tuple            =				generate_config.max_initial_timestamp_index
       else:
             UpperCamelCase_					: Union[str, Any]            =				model_config.vocab_size
       if self.max_initial_timestamp_index is None:
             UpperCamelCase_					: Any            =				model_config.vocab_size





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

       def handle_pairs(__lowerCAmelCase      ,  __lowerCAmelCase			):
             UpperCamelCase_					: Any            =				jnp.where((cur_len - self.begin_index) >= 1      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
             UpperCamelCase_					: Dict            =				jnp.where(
                 input_ids_k[cur_len - 1] >= self.timestamp_begin      ,  True and last_was_timestamp      ,  __lowerCAmelCase      ,  )

             UpperCamelCase_					: List[Any]            =				jnp.where((cur_len - self.begin_index) < 2      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
             UpperCamelCase_					: 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      ,  )

       UpperCamelCase_					: Tuple            =				jax.vmap(__lowerCAmelCase			)(__lowerCAmelCase      ,  __lowerCAmelCase			)

       UpperCamelCase_					: Union[str, Any]            =				jnp.where(cur_len == self.begin_index      ,  __lowerCAmelCase      ,  __lowerCAmelCase			)
       UpperCamelCase_					: str            =				jnp.where(
           self.max_initial_timestamp_index is not None      ,  True and apply_max_initial_timestamp      ,  __lowerCAmelCase      ,  )

       UpperCamelCase_					: str            =				self.timestamp_begin + self.max_initial_timestamp_index

       UpperCamelCase_					: Optional[int]            =				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
       UpperCamelCase_					: int            =				jax.nn.log_softmax(__lowerCAmelCase      ,  axis=-1			)

       def handle_cumulative_probs(__lowerCAmelCase      ,  __lowerCAmelCase			):
             UpperCamelCase_					: List[str]            =				jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]      ,  axis=-1			)
             UpperCamelCase_					: Tuple            =				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      ,  )

       UpperCamelCase_					: int            =				jax.vmap(__lowerCAmelCase			)(__lowerCAmelCase      ,  __lowerCAmelCase			)

       return scores














'''simple docstring'''

from __future__ import annotations

from math import pi, sqrt



def 				snake_case       (  a_				:			float     , a_				:			float						)							->					tuple:





      """simple docstring"""

      if inductance <= 0:
            raise ValueError("""Inductance cannot be 0 or negative"""						)

      elif capacitance <= 0:
            raise ValueError("""Capacitance cannot be 0 or negative"""						)

      else:
            return (
                "Resonant frequency",
                float(1 / (2 * pi * (sqrt(inductance * capacitance						)))						),
            )


if __name__ == "__main__":
      import doctest

      doctest.testmod()






