'''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__     :	List[Any]				=			get_logger(__name__)


UpperCamelCase__     :	Dict				=			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 				_lowercase	:
      '''simple docstring'''


      @add_start_docstrings(lowerCamelCase_							)
      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         raise NotImplementedError(
             f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''							)




class 				_lowercase	:
      '''simple docstring'''


      @add_start_docstrings(lowerCamelCase_							)
      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         raise NotImplementedError(
             f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.'''							)




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      @add_start_docstrings(lowerCamelCase_							)
      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         for processor in self:
            UpperCAmelCase__							:				Dict   				=  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__							:				Dict   				=  processor(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,**lowerCamelCase_							)
            else:
               UpperCAmelCase__							:				Optional[int]   				=  processor(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)
         return scores




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         if not isinstance(lowerCamelCase_					,lowerCamelCase_							) or not (temperature > 0):
            raise ValueError(f'''`temperature` has to be a strictly positive float, but is {temperature}'''							)

         UpperCAmelCase__							:				str   				=  temperature




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  scores / self.temperature
         return scores




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_ = -float('''Inf'''							)					,lowerCamelCase_ = 1							)			->			List[str]:



         '''simple docstring'''



         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__							:				Dict   				=  top_p
         UpperCAmelCase__							:				Tuple   				=  filter_value
         UpperCAmelCase__							:				Optional[Any]   				=  min_tokens_to_keep




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				List[str]   				=  lax.top_k(lowerCamelCase_					,scores.shape[-1]							)

         UpperCAmelCase__							:				Optional[Any]   				=  jnp.full_like(lowerCamelCase_					,self.filter_value							)
         UpperCAmelCase__							:				Union[str, Any]   				=  jax.nn.softmax(lowerCamelCase_					,axis=-1							).cumsum(axis=-1							)
         UpperCAmelCase__							:				str   				=  cumulative_probs < self.top_p

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

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

         UpperCAmelCase__							:				Any   				=  jnp.where(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)
         UpperCAmelCase__							:				Dict   				=  jax.lax.sort_key_val(lowerCamelCase_					,lowerCamelCase_							)[-1]

         return next_scores




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


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



         '''simple docstring'''



         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__							:				Union[str, Any]   				=  max(lowerCamelCase_					,lowerCamelCase_							)
         UpperCAmelCase__							:				Optional[int]   				=  filter_value




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         UpperCAmelCase__				,      UpperCAmelCase__							:				List[Any]   				=  scores.shape
         UpperCAmelCase__							:				Any   				=  jnp.full(batch_size * vocab_size					,self.filter_value							)

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

         UpperCAmelCase__							:				Optional[int]   				=  next_scores_flat.at[topk_indices_flat].set(lowerCamelCase_							)
         UpperCAmelCase__							:				List[Any]   				=  next_scores_flat.reshape(lowerCamelCase_					,lowerCamelCase_							)
         return next_scores




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_							)			->			Union[str, Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				str   				=  bos_token_id




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  jnp.full(scores.shape					,-float('''inf'''							)							)

         UpperCAmelCase__							:				str   				=  1 - jnp.bool_(cur_len - 1							)

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

         return scores




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  max_length
         UpperCAmelCase__							:				Union[str, Any]   				=  eos_token_id




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  jnp.full(scores.shape					,-float('''inf'''							)							)

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

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

         return scores




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         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__							:				Dict   				=  min_length
         UpperCAmelCase__							:				Optional[Any]   				=  eos_token_id




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  1 - jnp.clip(cur_len - self.min_length					,0					,1							)

         UpperCAmelCase__							:				Dict   				=  jnp.where(lowerCamelCase_					,scores.at[:, self.eos_token_id].set(-float('''inf'''							)							)					,lowerCamelCase_							)

         return scores




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_							)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  list(lowerCamelCase_							)
         UpperCAmelCase__							:				Tuple   				=  begin_index




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  1 - jnp.bool_(cur_len - self.begin_index							)

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

         return scores




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_							)			->			str:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  list(lowerCamelCase_							)




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         UpperCAmelCase__							:				List[Any]   				=  scores.at[..., self.suppress_tokens].set(-float('''inf'''							)							)

         return scores




class 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_							)			->			Optional[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  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__							:				Optional[Any]   				=  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__							:				int   				=  force_token_array.at[index].set(lowerCamelCase_							)
         UpperCAmelCase__							:				int   				=  jnp.intaa(lowerCamelCase_							)




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			jnp.ndarray:



         '''simple docstring'''



         def _force_token(lowerCamelCase_							):
            UpperCAmelCase__							:				List[Any]   				=  scores.shape[0]
            UpperCAmelCase__							:				List[Any]   				=  self.force_token_array[generation_idx]

            UpperCAmelCase__							:				int   				=  jnp.ones_like(lowerCamelCase_					,dtype=scores.dtype							) * -float('''inf'''							)
            UpperCAmelCase__							:				Any   				=  jnp.zeros((batch_size, 1)					,dtype=scores.dtype							)
            UpperCAmelCase__							:				Any   				=  lax.dynamic_update_slice(lowerCamelCase_					,lowerCamelCase_					,(0, current_token)							)
            return new_scores

         UpperCAmelCase__							:				Any   				=  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 				_lowercase	(      lowerCAmelCase   ):
      '''simple docstring'''


      def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Optional[int]:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  generate_config.eos_token_id
         UpperCAmelCase__							:				Union[str, Any]   				=  generate_config.no_timestamps_token_id
         UpperCAmelCase__							:				Tuple   				=  generate_config.no_timestamps_token_id + 1

         UpperCAmelCase__							:				Union[str, Any]   				=  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__							:				Optional[int]   				=  generate_config.max_initial_timestamp_index
         else:
            UpperCAmelCase__							:				Dict   				=  model_config.vocab_size
         if self.max_initial_timestamp_index is None:
            UpperCAmelCase__							:				Optional[Any]   				=  model_config.vocab_size




      def __call__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_							)			->			Any:



         '''simple docstring'''



         UpperCAmelCase__							:				Union[str, Any]   				=  scores.at[:, self.no_timestamps_token_id].set(-float('''inf'''							)							)

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

            UpperCAmelCase__							:				Any   				=  jnp.where((cur_len - self.begin_index) < 2					,lowerCamelCase_					,lowerCamelCase_							)
            UpperCAmelCase__							:				str   				=  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__							:				Union[str, Any]   				=  jax.vmap(lowerCamelCase_							)(lowerCamelCase_					,lowerCamelCase_							)

         UpperCAmelCase__							:				List[Any]   				=  jnp.where(cur_len == self.begin_index					,lowerCamelCase_					,lowerCamelCase_							)
         UpperCAmelCase__							:				Any   				=  jnp.where(
             self.max_initial_timestamp_index is not None					,True and apply_max_initial_timestamp					,lowerCamelCase_					,)

         UpperCAmelCase__							:				Any   				=  self.timestamp_begin + self.max_initial_timestamp_index

         UpperCAmelCase__							:				List[Any]   				=  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__							:				List[Any]   				=  jax.nn.log_softmax(lowerCamelCase_					,axis=-1							)

         def handle_cumulative_probs(lowerCamelCase_					,lowerCamelCase_							):
            UpperCAmelCase__							:				Tuple   				=  jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]					,axis=-1							)
            UpperCAmelCase__							:				Union[str, Any]   				=  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__							:				Optional[int]   				=  jax.vmap(lowerCamelCase_							)(lowerCamelCase_					,lowerCamelCase_							)

         return scores





'''simple docstring'''

import math







def __UpperCamelCase(   _A					:				float  ,			_A					:				float    ):
   '''simple docstring'''
   return math.pow(_A  ,			2    ) - a







def __UpperCamelCase(   _A					:				float    ):
   '''simple docstring'''
   return 2 * x







def __UpperCamelCase(   _A					:				float    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  2.0

   while start <= a:
      UpperCAmelCase__							:				int   				=  math.pow(_A  ,			2    )

   return start







def __UpperCamelCase(   _A					:				float  ,			_A					:				int = 99_99  ,			_A					:				float = 0.0_0_0_0_0_0_0_0_0_0_0_0_0_1    ):
   '''simple docstring'''

   if a < 0:
      raise ValueError('''math domain error'''    )

   UpperCAmelCase__							:				Any   				=  get_initial_point(_A    )

   for _ in range(_A    ):
      UpperCAmelCase__							:				Optional[Any]   				=  value
      UpperCAmelCase__							:				Tuple   				=  value - fx(_A  ,			_A    ) / fx_derivative(_A    )
      if abs(prev_value - value    ) < tolerance:
         return value

   return value


if __name__ == "__main__":
  from doctest import testmod

  testmod()




