'''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


__a: List[str]          =   get_logger(__name__)


__a: Union[str, Any]          =   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  :





       '''simple docstring'''



       @add_start_docstrings(__lowerCAmelCase )
       def __call__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  jnp.ndarray:
        raise NotImplementedError(
            F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )



class 			UpperCAmelCase  :





       '''simple docstring'''



       @add_start_docstrings(__lowerCAmelCase )
       def __call__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  jnp.ndarray:
        raise NotImplementedError(
            F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       @add_start_docstrings(__lowerCAmelCase )
       def __call__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )  ->  jnp.ndarray:
        for processor in self:
         lowercase__				:    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.""" )
          lowercase__				:    Union[str, Any]  =					processor(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   **__lowerCAmelCase )
         else:
          lowercase__				:    List[str]  =					processor(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )
        return scores



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



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

        lowercase__				:    Optional[Any]  =					temperature


       def __call__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  jnp.ndarray:
        lowercase__				:    Tuple  =					scores / self.temperature
        return scores



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = -float('''Inf''' )	,   __lowerCAmelCase = 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}""" )

        lowercase__				:    Any  =					top_p
        lowercase__				:    Optional[int]  =					filter_value
        lowercase__				:    Optional[Any]  =					min_tokens_to_keep


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

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

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

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

        lowercase__				:    List[str]  =					jnp.where(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )
        lowercase__				:    Optional[int]  =					jax.lax.sort_key_val(__lowerCAmelCase	,   __lowerCAmelCase )[-1]

        return next_scores



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



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

        lowercase__				:    Any  =					max(__lowerCAmelCase	,   __lowerCAmelCase )
        lowercase__				:    Dict  =					filter_value


       def __call__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  jnp.ndarray:
        lowercase__ ,							lowercase__				:    Optional[Any]  =					scores.shape
        lowercase__				:    Any  =					jnp.full(batch_size * vocab_size	,   self.filter_value )

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

        lowercase__				:    List[Any]  =					next_scores_flat.at[topk_indices_flat].set(__lowerCAmelCase )
        lowercase__				:    Union[str, Any]  =					next_scores_flat.reshape(__lowerCAmelCase	,   __lowerCAmelCase )
        return next_scores



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase )  ->  Union[str, Any]:
        lowercase__				:    List[Any]  =					bos_token_id


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

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

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

        return scores



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Optional[int]:
        lowercase__				:    int  =					max_length
        lowercase__				:    Tuple  =					eos_token_id


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

        lowercase__				:    Optional[Any]  =					1 - jnp.bool_(cur_len - self.max_length + 1 )

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

        return scores



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Optional[int]:
        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}""" )

        lowercase__				:    Optional[int]  =					min_length
        lowercase__				:    Any  =					eos_token_id


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

        lowercase__				:    Any  =					jnp.where(__lowerCAmelCase	,   scores.at[:, self.eos_token_id].set(-float('''inf''' ) )	,   __lowerCAmelCase )

        return scores



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Tuple:
        lowercase__				:    Optional[int]  =					list(__lowerCAmelCase )
        lowercase__				:    Tuple  =					begin_index


       def __call__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Tuple:
        lowercase__				:    Union[str, Any]  =					1 - jnp.bool_(cur_len - self.begin_index )

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

        return scores



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase )  ->  List[Any]:
        lowercase__				:    Optional[int]  =					list(__lowerCAmelCase )


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

        return scores



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase )  ->  Any:
        lowercase__				:    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.
        lowercase__				:    Optional[int]  =					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:
          lowercase__				:    Optional[int]  =					force_token_array.at[index].set(__lowerCAmelCase )
        lowercase__				:    Tuple  =					jnp.intaa(__lowerCAmelCase )


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

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

        lowercase__				:    Optional[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 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       def __init__(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )  ->  Any:
        lowercase__				:    List[Any]  =					generate_config.eos_token_id
        lowercase__				:    Dict  =					generate_config.no_timestamps_token_id
        lowercase__				:    Any  =					generate_config.no_timestamps_token_id + 1

        lowercase__				:    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''' ):
         lowercase__				:    str  =					generate_config.max_initial_timestamp_index
        else:
         lowercase__				:    int  =					model_config.vocab_size
        if self.max_initial_timestamp_index is None:
         lowercase__				:    List[str]  =					model_config.vocab_size


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

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

         lowercase__				:    List[str]  =					jnp.where((cur_len - self.begin_index) < 2	,   __lowerCAmelCase	,   __lowerCAmelCase )
         lowercase__				:    Union[str, 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	,   )

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

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

        lowercase__				:    Dict  =					self.timestamp_begin + self.max_initial_timestamp_index

        lowercase__				:    Union[str, 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
        lowercase__				:    Any  =					jax.nn.log_softmax(__lowerCAmelCase	,   axis=-1 )

        def handle_cumulative_probs(__lowerCAmelCase	,   __lowerCAmelCase ):
         lowercase__				:    Dict  =					jax.nn.logsumexp(logprobs_k[self.timestamp_begin :]	,   axis=-1 )
         lowercase__				:    Optional[int]  =					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	,   )

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

        return scores


'''simple docstring'''

import logging

import numpy as np
import pytest
from scipy.linalg import eigh

logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def 			__UpperCamelCase       (   UpperCAmelCase     ):
 return input_array.reshape((input_array.size, 1)     )
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Dict  =					np.nan
 for i in range(UpperCAmelCase     ):
  lowercase__				:    Optional[Any]  =					features[:, labels == i]
  lowercase__				:    Optional[Any]  =					data.mean(1     )
  # Centralize the data of class i
  lowercase__				:    Dict  =					data - column_reshape(UpperCAmelCase     )
  if i > 0:
   # If covariance_sum is not None
   covariance_sum += np.dot(UpperCAmelCase		,     centered_data.T     )
  else:
   # If covariance_sum is np.nan (i.e. first loop)
   lowercase__				:    List[str]  =					np.dot(UpperCAmelCase		,     centered_data.T     )

 return covariance_sum / features.shape[1]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 lowercase__				:    Tuple  =					features.mean(1     )
 lowercase__				:    Dict  =					np.nan
 for i in range(UpperCAmelCase     ):
  lowercase__				:    List[str]  =					features[:, labels == i]
  lowercase__				:    int  =					data.shape[1]
  lowercase__				:    Optional[int]  =					data.mean(1     )
  if i > 0:
   # If covariance_sum is not None
   covariance_sum += device_data * np.dot(
       column_reshape(UpperCAmelCase     ) - column_reshape(UpperCAmelCase     )		,     (column_reshape(UpperCAmelCase     ) - column_reshape(UpperCAmelCase     )).T		,     )
  else:
   # If covariance_sum is np.nan (i.e. first loop)
   lowercase__				:    Optional[int]  =					device_data * np.dot(
       column_reshape(UpperCAmelCase     ) - column_reshape(UpperCAmelCase     )		,     (column_reshape(UpperCAmelCase     ) - column_reshape(UpperCAmelCase     )).T		,     )

 return covariance_sum / features.shape[1]
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase     ):

 # Check if the features have been loaded
 if features.any():
  lowercase__				:    Optional[Any]  =					features.mean(1     )
  # Center the dataset
  lowercase__				:    List[str]  =					features - np.reshape(UpperCAmelCase		,     (data_mean.size, 1)     )
  lowercase__				:    Optional[Any]  =					np.dot(UpperCAmelCase		,     centered_data.T     ) / features.shape[1]
  lowercase__ ,							lowercase__				:    Tuple  =					np.linalg.eigh(UpperCAmelCase     )
  # Take all the columns in the reverse order (-1), and then takes only the first
  lowercase__				:    str  =					eigenvectors[:, ::-1][:, 0:dimensions]
  # Project the database on the new space
  lowercase__				:    Tuple  =					np.dot(filtered_eigenvectors.T		,     UpperCAmelCase     )
  logging.info('''Principal Component Analysis computed'''     )

  return projected_data
 else:
  logging.basicConfig(level=logging.ERROR		,     format='''%(message)s'''		,     force=UpperCAmelCase     )
  logging.error('''Dataset empty'''     )
  raise AssertionError
def 			__UpperCamelCase       (   UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     ):
 assert classes > dimensions

 # Check if features have been already loaded
 if features.any:
  lowercase__ ,							lowercase__				:    Any  =					eigh(
      covariance_between_classes(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )		,     covariance_within_classes(UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )		,     )
  lowercase__				:    Optional[int]  =					eigenvectors[:, ::-1][:, :dimensions]
  lowercase__ ,							lowercase__ ,							lowercase__				:    Optional[int]  =					np.linalg.svd(UpperCAmelCase     )
  lowercase__				:    List[str]  =					svd_matrix[:, 0:dimensions]
  lowercase__				:    str  =					np.dot(filtered_svd_matrix.T		,     UpperCAmelCase     )
  logging.info('''Linear Discriminant Analysis computed'''     )

  return projected_data
 else:
  logging.basicConfig(level=logging.ERROR		,     format='''%(message)s'''		,     force=UpperCAmelCase     )
  logging.error('''Dataset empty'''     )
  raise AssertionError
def 			__UpperCamelCase       (   ):
 # Create dummy dataset with 2 classes and 3 features
 lowercase__				:    List[str]  =					np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]]     )
 lowercase__				:    Optional[Any]  =					np.array([0, 0, 0, 1, 1]     )
 lowercase__				:    str  =					2
 lowercase__				:    Dict  =					2

 # Assert that the function raises an AssertionError if dimensions > classes
 with pytest.raises(UpperCAmelCase     ) as error_info:
  lowercase__				:    int  =					linear_discriminant_analysis(
      UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase     )
  if isinstance(UpperCAmelCase		,     np.ndarray     ):
   raise AssertionError(
       '''Did not raise AssertionError for dimensions > classes'''     )
  assert error_info.type is AssertionError
def 			__UpperCamelCase       (   ):
 lowercase__				:    Optional[int]  =					np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]     )
 lowercase__				:    int  =					2
 lowercase__				:    Any  =					np.array([[6.9_2_8_2_0_3_2_3, 8.6_6_0_2_5_4_0_4, 1_0.3_9_2_3_0_4_8_5], [3.0, 3.0, 3.0]]     )

 with pytest.raises(UpperCAmelCase     ) as error_info:
  lowercase__				:    Dict  =					principal_component_analysis(UpperCAmelCase		,     UpperCAmelCase     )
  if not np.allclose(UpperCAmelCase		,     UpperCAmelCase     ):
   raise AssertionError
  assert error_info.type is AssertionError


if __name__ == "__main__":
  import doctest

  doctest.testmod()

