


'''simple docstring'''






import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
 import torch

 from transformers.generation import (
     MaxLengthCriteria,
     MaxNewTokensCriteria,
     MaxTimeCriteria,
     StoppingCriteriaList,
     validate_stopping_criteria,
 )




@require_torch
class  a     (     unittest.TestCase       ):
 def 							__UpperCAmelCase		( self      ,				__magic_name__						)					->							Union[str, Any]:
      _a			      =							3
      _a			      =							2_50

      _a			      =							ids_tensor((batch_size, length)      ,				__magic_name__						)
      _a			      =							torch.ones((batch_size, length)      ,				device=__magic_name__      ,				dtype=torch.float						) / length
      return input_ids, scores
 def 							__UpperCAmelCase		( self						)					->							str:
      _a      ,   _a			      =							self._get_tensors(5						)

      _a			      =							StoppingCriteriaList(
          [
              MaxLengthCriteria(max_length=10						),
              MaxTimeCriteria(max_time=0.1						),
          ]						)

      self.assertFalse(criteria(__magic_name__      ,				__magic_name__						)						)

      _a      ,   _a			      =							self._get_tensors(9						)
      self.assertFalse(criteria(__magic_name__      ,				__magic_name__						)						)

      _a      ,   _a			      =							self._get_tensors(10						)
      self.assertTrue(criteria(__magic_name__      ,				__magic_name__						)						)
 def 							__UpperCAmelCase		( self						)					->							str:
      _a			      =							MaxLengthCriteria(max_length=10						)

      _a      ,   _a			      =							self._get_tensors(5						)
      self.assertFalse(criteria(__magic_name__      ,				__magic_name__						)						)

      _a      ,   _a			      =							self._get_tensors(9						)
      self.assertFalse(criteria(__magic_name__      ,				__magic_name__						)						)

      _a      ,   _a			      =							self._get_tensors(10						)
      self.assertTrue(criteria(__magic_name__      ,				__magic_name__						)						)
 def 							__UpperCAmelCase		( self						)					->							Union[str, Any]:
      _a			      =							MaxNewTokensCriteria(start_length=5      ,				max_new_tokens=5						)

      _a      ,   _a			      =							self._get_tensors(5						)
      self.assertFalse(criteria(__magic_name__      ,				__magic_name__						)						)

      _a      ,   _a			      =							self._get_tensors(9						)
      self.assertFalse(criteria(__magic_name__      ,				__magic_name__						)						)

      _a      ,   _a			      =							self._get_tensors(10						)
      self.assertTrue(criteria(__magic_name__      ,				__magic_name__						)						)

      _a			      =							StoppingCriteriaList([criteria]						)
      self.assertEqual(criteria_list.max_length      ,				10						)
 def 							__UpperCAmelCase		( self						)					->							str:
      _a      ,   _a			      =							self._get_tensors(5						)

      _a			      =							MaxTimeCriteria(max_time=0.1						)
      self.assertFalse(criteria(__magic_name__      ,				__magic_name__						)						)

      _a			      =							MaxTimeCriteria(max_time=0.1      ,				initial_timestamp=time.time() - 0.2						)
      self.assertTrue(criteria(__magic_name__      ,				__magic_name__						)						)




 def 							__UpperCAmelCase		( self						)					->							List[str]:
      validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10						)]						)      ,				10						)

      with self.assertWarns(__magic_name__						):
           validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10						)]						)      ,				11						)

      _a			      =							validate_stopping_criteria(StoppingCriteriaList()      ,				11						)

      self.assertEqual(len(__magic_name__						)      ,				1						)





'''simple docstring'''






import warnings

from ...utils import logging
from .image_processing_donut import DonutImageProcessor


a_					:    Optional[Any]          =    logging.get_logger(__name__)




class  a     (     _SCREAMING_SNAKE_CASE       ):
 def __init__( self      ,				*__magic_name__      ,				**__magic_name__						)					->							None:
      warnings.warn(
          'The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
          ' use DonutImageProcessor instead.'      ,				__magic_name__      ,				)
      super().__init__(*__magic_name__      ,				**__magic_name__						)

