


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     lowercase__  (					unittest.TestCase		):






  def 			_UpperCAmelCase  (					self      :					Any		,						_lowercase      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     3
         UpperCAmelCase__										=     2_50

         UpperCAmelCase__										=     ids_tensor((batch_size, length)		,						_lowercase )
         UpperCAmelCase__										=     torch.ones((batch_size, length)		,						device=_lowercase		,						dtype=torch.float ) / length
         return input_ids, scores






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self._get_tensors(5 )

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

         self.assertFalse(criteria(_lowercase		,						_lowercase ) )

         UpperCAmelCase__						,			UpperCAmelCase__										=     self._get_tensors(9 )
         self.assertFalse(criteria(_lowercase		,						_lowercase ) )

         UpperCAmelCase__						,			UpperCAmelCase__										=     self._get_tensors(10 )
         self.assertTrue(criteria(_lowercase		,						_lowercase ) )






  def 			_UpperCAmelCase  (					self      :					List[str] ):




         """simple docstring"""







         UpperCAmelCase__										=     MaxLengthCriteria(max_length=10 )

         UpperCAmelCase__						,			UpperCAmelCase__										=     self._get_tensors(5 )
         self.assertFalse(criteria(_lowercase		,						_lowercase ) )

         UpperCAmelCase__						,			UpperCAmelCase__										=     self._get_tensors(9 )
         self.assertFalse(criteria(_lowercase		,						_lowercase ) )

         UpperCAmelCase__						,			UpperCAmelCase__										=     self._get_tensors(10 )
         self.assertTrue(criteria(_lowercase		,						_lowercase ) )






  def 			_UpperCAmelCase  (					self      :					Optional[int] ):




         """simple docstring"""







         UpperCAmelCase__										=     MaxNewTokensCriteria(start_length=5		,						max_new_tokens=5 )

         UpperCAmelCase__						,			UpperCAmelCase__										=     self._get_tensors(5 )
         self.assertFalse(criteria(_lowercase		,						_lowercase ) )

         UpperCAmelCase__						,			UpperCAmelCase__										=     self._get_tensors(9 )
         self.assertFalse(criteria(_lowercase		,						_lowercase ) )

         UpperCAmelCase__						,			UpperCAmelCase__										=     self._get_tensors(10 )
         self.assertTrue(criteria(_lowercase		,						_lowercase ) )

         UpperCAmelCase__										=     StoppingCriteriaList([criteria] )
         self.assertEqual(criteria_list.max_length		,						10 )






  def 			_UpperCAmelCase  (					self      :					Optional[Any] ):




         """simple docstring"""







         UpperCAmelCase__						,			UpperCAmelCase__										=     self._get_tensors(5 )

         UpperCAmelCase__										=     MaxTimeCriteria(max_time=0.1 )
         self.assertFalse(criteria(_lowercase		,						_lowercase ) )

         UpperCAmelCase__										=     MaxTimeCriteria(max_time=0.1		,						initial_timestamp=time.time() - 0.2 )
         self.assertTrue(criteria(_lowercase		,						_lowercase ) )






  def 			_UpperCAmelCase  (					self      :					int ):




         """simple docstring"""







         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10 )] )		,						10 )

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

         UpperCAmelCase__										=     validate_stopping_criteria(StoppingCriteriaList()		,						11 )

         self.assertEqual(len(_lowercase )		,						1 )









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


A      = logging.getLogger(__name__)


@dataclass
class     lowercase__  (					__SCREAMING_SNAKE_CASE		):
  A__=  field(
      default=0.0					,							metadata={'help': 'The label smoothing epsilon to apply (if not zero).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to SortishSamler or not.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Whether to use generate to calculate generative metrics (ROUGE, BLEU).'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'whether to use adafactor'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Encoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Decoder layer dropout probability. Goes into model.config.'}		)
  A__=  field(default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Dropout probability. Goes into model.config.'}		)
  A__=  field(
      default=__SCREAMING_SNAKE_CASE					,							metadata={'help': 'Attention dropout probability. Goes into model.config.'}		)
  A__=  field(
      default='linear'					,							metadata={'help': f'Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}'}					,							)





