



"""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        __snake_case       (	unittest.TestCase):




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]						,					__lowerCAmelCase     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Any    			= 3
         _lowerCamelCase						:				str    			= 2_5_0

         _lowerCamelCase						:				Optional[Any]    			= ids_tensor((batch_size, length)						,					__lowerCAmelCase		)
         _lowerCamelCase						:				List[str]    			= torch.ones((batch_size, length)						,					device=__lowerCAmelCase						,					dtype=torch.float		) / length
         return input_ids, scores




  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






         _lowerCamelCase			,    _lowerCamelCase						:				List[str]    			= self._get_tensors(5		)

         _lowerCamelCase						:				str    			= StoppingCriteriaList(
             [
                 MaxLengthCriteria(max_length=1_0		),
                 MaxTimeCriteria(max_time=0.1		),
             ]		)

         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				List[Any]    			= self._get_tensors(9		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				Any    			= self._get_tensors(1_0		)
         self.assertTrue(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						Optional[int]		):






         """simple docstring"""






         _lowerCamelCase						:				Optional[Any]    			= MaxLengthCriteria(max_length=1_0		)

         _lowerCamelCase			,    _lowerCamelCase						:				Optional[int]    			= self._get_tensors(5		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				Any    			= self._get_tensors(9		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				int    			= self._get_tensors(1_0		)
         self.assertTrue(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						int		):






         """simple docstring"""






         _lowerCamelCase						:				Tuple    			= MaxNewTokensCriteria(start_length=5						,					max_new_tokens=5		)

         _lowerCamelCase			,    _lowerCamelCase						:				Tuple    			= self._get_tensors(5		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				Tuple    			= self._get_tensors(9		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase			,    _lowerCamelCase						:				Any    			= self._get_tensors(1_0		)
         self.assertTrue(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase						:				str    			= StoppingCriteriaList([criteria]		)
         self.assertEqual(criteria_list.max_length						,					1_0		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[Any]		):






         """simple docstring"""






         _lowerCamelCase			,    _lowerCamelCase						:				Optional[Any]    			= self._get_tensors(5		)

         _lowerCamelCase						:				List[str]    			= MaxTimeCriteria(max_time=0.1		)
         self.assertFalse(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)

         _lowerCamelCase						:				Dict    			= MaxTimeCriteria(max_time=0.1						,					initial_timestamp=time.time() - 0.2		)
         self.assertTrue(criteria(__lowerCAmelCase						,					__lowerCAmelCase		)		)




  def 						SCREAMING_SNAKE_CASE				( self     :						List[str]		):






         """simple docstring"""






         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0		)]		)						,					1_0		)

         with self.assertWarns(__lowerCAmelCase		):
                validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(1_0		)]		)						,					1_1		)

         _lowerCamelCase						:				Dict    			= validate_stopping_criteria(StoppingCriteriaList()						,					1_1		)

         self.assertEqual(len(__lowerCAmelCase		)						,					1		)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    List[Any]						    =  {
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
         'BioGptForCausalLM',
         'BioGptForTokenClassification',
         'BioGptForSequenceClassification',
         'BioGptModel',
         'BioGptPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _snake_case  :    List[str]						    =  _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

