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


A_     :						List[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     __UpperCAmelCase       ( __a		):





  @add_start_docstrings(_lowerCamelCase )
  def __call__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     raise NotImplementedError('''StoppingCriteria needs to be subclassed''' )






class     __UpperCAmelCase       ( __a		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      max_length
     lowerCAmelCase_			=      max_position_embeddings







  @add_start_docstrings(_lowerCamelCase )
  def __call__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     lowerCAmelCase_			=      input_ids.shape[-1]
     lowerCAmelCase_			=      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     __UpperCAmelCase       ( __a		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase ):
     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.'''    ,     _lowerCamelCase    ,     )
     lowerCAmelCase_			=      start_length
     lowerCAmelCase_			=      max_new_tokens
     lowerCAmelCase_			=      start_length + max_new_tokens







  @add_start_docstrings(_lowerCamelCase )
  def __call__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     return input_ids.shape[-1] >= self.max_length






class     __UpperCAmelCase       ( __a		):





  def __init__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase = None ):
     lowerCAmelCase_			=      max_time
     lowerCAmelCase_			=      time.time() if initial_timestamp is None else initial_timestamp







  @add_start_docstrings(_lowerCamelCase )
  def __call__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     return time.time() - self.initial_timestamp > self.max_time






class     __UpperCAmelCase       ( __a		):





  @add_start_docstrings(_lowerCamelCase )
  def __call__(	self    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     **_lowerCamelCase ):
     return any(criteria(_lowerCamelCase    ,     _lowerCamelCase ) for criteria in self )







  @property
  def 						UpperCAmelCase_	(	self ):
     for stopping_criterium in self:
        if isinstance(_lowerCamelCase    ,     _lowerCamelCase ):
           return stopping_criterium.max_length
        elif isinstance(_lowerCamelCase    ,     _lowerCamelCase ):
           return stopping_criterium.max_length
     return None






def snake_case_      (					__snake_case  :							StoppingCriteriaList	,   __snake_case  :							int)			->					StoppingCriteriaList:
   lowerCAmelCase_			=      stopping_criteria.max_length
   lowerCAmelCase_			=      deepcopy(__snake_case)
   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'''	,   __snake_case)
   elif stopping_max_length is None:
      new_stopping_criteria.append(MaxLengthCriteria(max_length=__snake_case))
   return new_stopping_criteria






'''simple docstring'''

import gc
import threading
import time

import psutil
import torch


class     __UpperCAmelCase       :





  def __init__(	self ):
     lowerCAmelCase_			=      psutil.Process()
     lowerCAmelCase_			=      False





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      -1

     while True:
        lowerCAmelCase_			=      max(self.process.memory_info().rss    ,     self.cpu_memory_peak )

        # can't sleep or will not catch the peak right (this comment is here on purpose)
        if not self.peak_monitoring:
           break





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      True
     lowerCAmelCase_			=      threading.Thread(target=self.peak_monitor )
     lowerCAmelCase_			=      True
     self.thread.start()







  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      False
     self.thread.join()
     return self.cpu_memory_peak






A_     :						List[str]    							=PeakCPUMemory()



def snake_case_      (					)			->					Tuple:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time()}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   lowerCAmelCase_			=      psutil.Process().memory_info().rss
   cpu_peak_tracker.start()

   # GPU mem
   for i in range(torch.cuda.device_count()):
      lowerCAmelCase_			=      torch.cuda.memory_allocated(__snake_case)
   torch.cuda.reset_peak_memory_stats()

   return measures



def snake_case_      (					__snake_case  :							Any)			->					List[str]:
   # Time
   lowerCAmelCase_			=      {'''time''': time.time() - start_measures['''time''']}

   gc.collect()
   torch.cuda.empty_cache()

   # CPU mem
   lowerCAmelCase_			=      (psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
   lowerCAmelCase_			=      (cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20

   # GPU mem
   for i in range(torch.cuda.device_count()):
      lowerCAmelCase_			=      (torch.cuda.memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20
      lowerCAmelCase_			=      (torch.cuda.max_memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20

   return measures






def snake_case_      (					__snake_case  :							Dict	,   __snake_case  :							Optional[int])			->					Dict:
   print(F'''{description}:''')
   print(F'''- Time: {measures['time']:.2f}s''')
   for i in range(torch.cuda.device_count()):
      print(F'''- GPU {i} allocated: {measures[str(__snake_case)]:.2f}MiB''')
      lowerCAmelCase_			=      measures[F'''{i}-peak''']
      print(F'''- GPU {i} peak: {peak:.2f}MiB''')
   print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB''')
   print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB''')





