




'''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


a       :						int								=		logging.get_logger(__name__)


a       :						Any								=		r'''
    Args:
        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
            Indices of input sequence tokens in the vocabulary.

            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
            [`PreTrainedTokenizer.__call__`] for details.

            [What are input IDs?](../glossary#input-ids)
        scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
            Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
            or scores for each vocabulary token after SoftMax.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional stopping criteria specific kwargs.

    Return:
        `bool`. `False` indicates we should continue, `True` indicates we should stop.

'''



class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			@add_start_docstrings(a_							)
			def __call__(      self						:    int      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Tuple							):



						"""simple docstring"""




						raise NotImplementedError("StoppingCriteria needs to be subclassed"							)







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    Dict      ,		a_						:    int      ,		a_						:    Optional[int] = None							):



						"""simple docstring"""




						__snake_case			=		max_length
						__snake_case			=		max_position_embeddings






			@add_start_docstrings(a_							)
			def __call__(      self						:    str      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Union[str, Any]							):



						"""simple docstring"""




						__snake_case			=		input_ids.shape[-1]
						__snake_case			=		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 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    List[str]      ,		a_						:    int      ,		a_						:    int							):



						"""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."      ,		a_      ,		)
						__snake_case			=		start_length
						__snake_case			=		max_new_tokens
						__snake_case			=		start_length + max_new_tokens






			@add_start_docstrings(a_							)
			def __call__(      self						:    Union[str, Any]      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Tuple							):



						"""simple docstring"""




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







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			def __init__(      self						:    Tuple      ,		a_						:    float      ,		a_						:    Optional[float] = None							):



						"""simple docstring"""




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






			@add_start_docstrings(a_							)
			def __call__(      self						:    str      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    str							):



						"""simple docstring"""




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







class 		SCREAMING_SNAKE_CASE__  (			_UpperCamelCase	):





			@add_start_docstrings(a_							)
			def __call__(      self						:    Tuple      ,		a_						:    torch.LongTensor      ,		a_						:    torch.FloatTensor      ,		**a_						:    Tuple							):



						"""simple docstring"""




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






			@property
			def 							A (      self						:    Any							):



						"""simple docstring"""




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







def __UpperCAmelCase	(   _UpperCAmelCase		:   StoppingCriteriaList		,				_UpperCAmelCase		:   int						)      ->      StoppingCriteriaList:
			__snake_case			=		stopping_criteria.max_length
			__snake_case			=		deepcopy(_UpperCAmelCase						)
			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"		,				_UpperCAmelCase						)
			elif stopping_max_length is None:
						new_stopping_criteria.append(MaxLengthCriteria(max_length=_UpperCAmelCase						)						)
			return new_stopping_criteria











from math import sqrt

import numpy as np
from sympy import symbols

# Coefficient
# Speed of light (m/s)
_lowerCamelCase      :						str							=  299792458

# Symbols
_lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase			,     _lowerCamelCase      :						int							=  symbols("""ct x y z""")







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			float:



       """simple docstring"""

       if velocity > c:
              raise ValueError('''Speed must not exceed light speed 299,792,458 [m/s]!'''		)
       elif velocity < 1:
              # Usually the speed should be much higher than 1 (c order of magnitude)
              raise ValueError('''Speed must be greater than or equal to 1!'''		)

       return velocity / c







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			float:



       """simple docstring"""

       return 1 / sqrt(1 - beta(lowercase_		) ** 2		)







def SCREAMING_SNAKE_CASE							(			lowercase_		)  ->			np.ndarray:



       """simple docstring"""

       return np.array(
           [
               [gamma(lowercase_		), -gamma(lowercase_		) * beta(lowercase_		), 0, 0],
               [-gamma(lowercase_		) * beta(lowercase_		), gamma(lowercase_		), 0, 0],
               [0, 0, 1, 0],
               [0, 0, 0, 1],
           ]		)







def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_ = None		)  ->			np.ndarray:



       """simple docstring"""

       if event is None:
              A__ 					=       np.array([ct, x, y, z]		)  # Symbolic four vector
       else:
              event[0] *= c  # x0 is ct (speed of light * time)

       return transformation_matrix(lowercase_		) @ event


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # Example of symbolic vector:
  _lowerCamelCase      :						Tuple							=  transform(29979245)
  print("""Example of four vector: """)
  print(F'''ct\' = {four_vector[0]}''')
  print(F'''x\' = {four_vector[1]}''')
  print(F'''y\' = {four_vector[2]}''')
  print(F'''z\' = {four_vector[3]}''')

  # Substitute symbols with numerical values
  _lowerCamelCase      :						int							=  {ct: c, x: 1, y: 1, z: 1}
  _lowerCamelCase      :						Any							=  [four_vector[i].subs(sub_dict) for i in range(4)]

  print(F'''\n{numerical_vector}''')


