


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 		__magic_name__						(unittest.TestCase				):





     '''simple docstring'''



     def 	SCREAMING_SNAKE_CASE__						(					self:str     ,		_a:Dict   ):
       snake_case__						= 3
       snake_case__						= 2_50

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

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__				,				snake_case__						= self._get_tensors(5   )

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

       self.assertFalse(criteria(_a     ,		_a   )   )

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:List[Any]   ):
       snake_case__						= MaxLengthCriteria(max_length=10   )

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

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= MaxNewTokensCriteria(start_length=5     ,		max_new_tokens=5   )

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

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

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

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

     def 	SCREAMING_SNAKE_CASE__						(					self:str   ):
       snake_case__				,				snake_case__						= self._get_tensors(5   )

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

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







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )     ,		10   )

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

       snake_case__						= validate_stopping_criteria(StoppingCriteriaList()     ,		11   )

       self.assertEqual(len(_a   )     ,		1   )



from ...utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_torch_available,
    is_transformers_available,
)


try:
 if not (is_transformers_available() and is_torch_available()):
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
 from .multicontrolnet import MultiControlNetModel
 from .pipeline_controlnet import StableDiffusionControlNetPipeline
 from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
 from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline


if is_transformers_available() and is_flax_available():
 from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline


