



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"""
    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 	lowerCamelCase__	:



 """simple docstring"""

 @add_start_docstrings(__lowerCAmelCase      )
 def __call__(    self					:					List[Any]			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray      )   ->	jnp.ndarray:
    raise NotImplementedError(
        f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )


class 	lowerCamelCase__	:



 """simple docstring"""

 @add_start_docstrings(__lowerCAmelCase      )
 def __call__(    self					:					List[str]			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray      )   ->	jnp.ndarray:
    raise NotImplementedError(
        f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''      )


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 @add_start_docstrings(__lowerCAmelCase      )
 def __call__(    self					:					Optional[int]			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					int			,						**__lowerCAmelCase					:					Tuple      )   ->	jnp.ndarray:
    for processor in self:
       _A      =       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.'''      )
          _A      =       processor(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase			,						**__lowerCAmelCase      )
       else:
          _A      =       processor(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )
    return scores


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

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

    _A      =       temperature

 def __call__(    self					:					Optional[int]			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					int      )   ->	jnp.ndarray:
    _A      =       scores / self.temperature
    return scores


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					str			,						__lowerCAmelCase					:					float			,						__lowerCAmelCase					:					float = -float('''Inf'''      )			,						__lowerCAmelCase					:					int = 1      )   ->	Any:
    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}'''      )

    _A      =       top_p
    _A      =       filter_value
    _A      =       min_tokens_to_keep

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

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

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

    _A      =       jnp.where(__lowerCAmelCase			,						__lowerCAmelCase			,						__lowerCAmelCase      )
    _A      =       jax.lax.sort_key_val(__lowerCAmelCase			,						__lowerCAmelCase      )[-1]

    return next_scores


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					float = -float('''Inf'''      )			,						__lowerCAmelCase					:					int = 1      )   ->	Optional[Any]:
    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}'''      )

    _A      =       max(__lowerCAmelCase			,						__lowerCAmelCase      )
    _A      =       filter_value

 def __call__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					int      )   ->	jnp.ndarray:
    _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(__lowerCAmelCase			,						__lowerCAmelCase      )
    _A      =       jnp.broadcast_to((jnp.arange(__lowerCAmelCase      ) * 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(__lowerCAmelCase      )
    _A      =       next_scores_flat.reshape(__lowerCAmelCase			,						__lowerCAmelCase      )
    return next_scores


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					str			,						__lowerCAmelCase					:					int      )   ->	int:
    _A      =       bos_token_id

 def __call__(    self					:					Union[str, Any]			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					int      )   ->	jnp.ndarray:
    _A      =       jnp.full(scores.shape			,						-float('''inf'''      )      )

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

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

    return scores


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					int			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int      )   ->	Any:
    _A      =       max_length
    _A      =       eos_token_id

 def __call__(    self					:					List[str]			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					int      )   ->	jnp.ndarray:
    _A      =       jnp.full(scores.shape			,						-float('''inf'''      )      )

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

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

    return scores


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Tuple			,						__lowerCAmelCase					:					int			,						__lowerCAmelCase					:					int      )   ->	Any:
    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}'''      )

    _A      =       min_length
    _A      =       eos_token_id

 def __call__(    self					:					Dict			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					int      )   ->	jnp.ndarray:
    # 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(__lowerCAmelCase			,						scores.at[:, self.eos_token_id].set(-float('''inf'''      )      )			,						__lowerCAmelCase      )

    return scores


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					Any			,						__lowerCAmelCase					:					Optional[int]      )   ->	Union[str, Any]:
    _A      =       list(__lowerCAmelCase      )
    _A      =       begin_index

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

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

    return scores


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					int			,						__lowerCAmelCase					:					list      )   ->	Dict:
    _A      =       list(__lowerCAmelCase      )

 def __call__(    self					:					str			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					int      )   ->	jnp.ndarray:
    _A      =       scores.at[..., self.suppress_tokens].set(-float('''inf'''      )      )

    return scores


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

 def __init__(    self					:					int			,						__lowerCAmelCase					:					Optional[Any]      )   ->	List[Any]:
    _A      =       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.
    _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(__lowerCAmelCase      )
    _A      =       jnp.intaa(__lowerCAmelCase      )

 def __call__(    self					:					Optional[Any]			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					jnp.ndarray			,						__lowerCAmelCase					:					int      )   ->	jnp.ndarray:
    def _force_token(__lowerCAmelCase					:					List[Any]      ):
       _A      =       scores.shape[0]
       _A      =       self.force_token_array[generation_idx]

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


class 	lowerCamelCase__	(       _A):



 """simple docstring"""

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

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

       _A      =       jnp.where((cur_len - self.begin_index) < 2			,						__lowerCAmelCase			,						__lowerCAmelCase      )
       _A      =       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			,						)

    _A      =       jax.vmap(__lowerCAmelCase      )(__lowerCAmelCase			,						__lowerCAmelCase      )

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

    _A      =       self.timestamp_begin + self.max_initial_timestamp_index

    _A      =       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
    _A      =       jax.nn.log_softmax(__lowerCAmelCase			,						axis=-1      )

    def handle_cumulative_probs(__lowerCAmelCase					:					Optional[Any]			,						__lowerCAmelCase					:					str      ):
       _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'''      )      )			,						__lowerCAmelCase			,						)

    _A      =       jax.vmap(__lowerCAmelCase      )(__lowerCAmelCase			,						__lowerCAmelCase      )

    return scores








import itertools
import math

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int				)  ->     bool:

   if 1 < number < 4:
      # 2 and 3 are primes
      return True
   elif number < 2 or number % 2 == 0 or number % 3 == 0:
      # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
      return False

   # All primes number are in format of 6k +/- 1
   for i in range(5	,   int(math.sqrt(_snake_case				) + 1				)	,   6				):
      if number % i == 0 or number % (i + 2) == 0:
         return False
   return True

def 	SCREAMING_SNAKE_CASE_							(	)  ->     Dict:
   _A      =       2
   while True:
      if is_prime(_snake_case				):
         yield num
      num += 1

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:int = 10_001				)  ->     int:
   return next(itertools.islice(prime_generator()	,   nth - 1	,   _snake_case				)				)


if __name__ == "__main__":
   print(f'{solution() = }')



