"""simple docstring"""


import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional

import torch

from ..utils import add_start_docstrings, logging


lowerCAmelCase_:					Optional[int]     =					logging.get_logger(__name__)


lowerCAmelCase_:					Union[str, Any]     =					R"\n    Args:\n        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):\n            Indices of input sequence tokens in the vocabulary.\n\n            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and\n            [`PreTrainedTokenizer.__call__`] for details.\n\n            [What are input IDs?](../glossary#input-ids)\n        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):\n            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax\n            or scores for each vocabulary token after SoftMax.\n        kwargs (`Dict[str, Any]`, *optional*):\n            Additional stopping criteria specific kwargs.\n\n    Return:\n        `bool`. `False` indicates we should continue, `True` indicates we should stop.\n\n"






class    a__    (   _a	):


   @add_start_docstrings(_UpperCAmelCase  )
   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ):
        '''simple docstring'''



        raise NotImplementedError("StoppingCriteria needs to be subclassed"  )






class    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None  ):
        '''simple docstring'''



        lowercase__				 =							max_length
        lowercase__				 =							max_position_embeddings


   @add_start_docstrings(_UpperCAmelCase  )
   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ):
        '''simple docstring'''



        lowercase__				 =							input_ids.shape[-1]
        lowercase__				 =							cur_len >= self.max_length
        if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
             logger.warning_once(
                 "This is a friendly reminder - the current text generation call will exceed the model's predefined "
                 F'''maximum length ({self.max_position_embeddings}). Depending on the model, you may observe '''
                 "exceptions, performance degradation, or nothing at all."  )
        return is_done






class    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase  ):
        '''simple docstring'''



        warnings.warn(
            "The class `MaxNewTokensCriteria` is deprecated. "
            F'''Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` '''
            "with `max_length = start_length + max_new_tokens` instead.",				_UpperCAmelCase,				)
        lowercase__				 =							start_length
        lowercase__				 =							max_new_tokens
        lowercase__				 =							start_length + max_new_tokens


   @add_start_docstrings(_UpperCAmelCase  )
   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ):
        '''simple docstring'''



        return input_ids.shape[-1] >= self.max_length






class    a__    (   _a	):


   def __init__(     self,				_UpperCAmelCase,				_UpperCAmelCase = None  ):
        '''simple docstring'''



        lowercase__				 =							max_time
        lowercase__				 =							time.time() if initial_timestamp is None else initial_timestamp


   @add_start_docstrings(_UpperCAmelCase  )
   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ):
        '''simple docstring'''



        return time.time() - self.initial_timestamp > self.max_time






class    a__    (   _a	):


   @add_start_docstrings(_UpperCAmelCase  )
   def __call__(     self,				_UpperCAmelCase,				_UpperCAmelCase,				**_UpperCAmelCase  ):
        '''simple docstring'''



        return any(criteria(_UpperCAmelCase,				_UpperCAmelCase  ) for criteria in self  )


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        for stopping_criterium in self:
             if isinstance(_UpperCAmelCase,				_UpperCAmelCase  ):
                  return stopping_criterium.max_length
             elif isinstance(_UpperCAmelCase,				_UpperCAmelCase  ):
                  return stopping_criterium.max_length
        return None







def 					__a   (		A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							stopping_criteria.max_length
     lowercase__				 =							deepcopy(A      )
     if stopping_max_length is not None and stopping_max_length != max_length:
          warnings.warn("You set different `max_length` for stopping criteria and `max_length` parameter"    ,				A      )
     elif stopping_max_length is None:
          new_stopping_criteria.append(MaxLengthCriteria(max_length=A      )      )
     return new_stopping_criteria





"""simple docstring"""


from sympy import diff, lambdify, symbols
from sympy.functions import *  # noqa: F403







def 					__a   (		A    ,				A    ,				A = "x"    ,				A = 10**-10    ,				A = 1    ,				):






     '''simple docstring'''



     lowercase__				 =							symbols(A      )
     lowercase__				 =							lambdify(A    ,				A      )
     lowercase__				 =							lambdify(A    ,				diff(A    ,				A      )      )

     lowercase__				 =							starting_point

     while True:
          if diff_function(A      ) != 0:
               lowercase__				 =							prev_guess - multiplicity * func(A      ) / diff_function(
                   A      )
          else:
               raise ZeroDivisionError("Could not find root"      ) from None

          # Precision is checked by comparing the difference of consecutive guesses
          if abs(next_guess - prev_guess      ) < precision:
               return next_guess

          lowercase__				 =							next_guess


# Let's Execute
if __name__ == "__main__":
 # Find root of trigonometric function
 # Find value of pi
 print(F'The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}')

 # Find root of polynomial
 # Find fourth Root of 5
 print(F'The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}')

 # Find value of e
 print(
     "The root of log(y) - 1 = 0 is ",
     F'{newton_raphson("log(y) - 1", 2, variable="y")}',
 )

 # Exponential Roots
 print(
     "The root of exp(x) - 1 = 0 is",
     F'{newton_raphson("exp(x) - 1", 1_0, precision=0.005)}',
 )

 # Find root of cos(x)
 print(F'The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}')




