






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  SCREAMING_SNAKE_CASE			(			unittest.TestCase					):
     '''simple docstring'''


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      int							=    3
       SCREAMING_SNAKE_CASE       :      List[Any]							=    250

       SCREAMING_SNAKE_CASE       :      str							=    ids_tensor((batch_size, length)					, UpperCAmelCase_      )
       SCREAMING_SNAKE_CASE       :      str							=    torch.ones((batch_size, length)					, device=UpperCAmelCase_					, dtype=torch.float      ) / length
       return input_ids, scores


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[Any]							=    self._get_tensors(5      )

       SCREAMING_SNAKE_CASE       :      List[Any]							=    StoppingCriteriaList(
           [
               MaxLengthCriteria(max_length=10      ),
               MaxTimeCriteria(max_time=0.1      ),
           ]      )

       self.assertFalse(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )

       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    self._get_tensors(9      )
       self.assertFalse(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )

       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[Any]							=    self._get_tensors(10      )
       self.assertTrue(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )


     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    MaxLengthCriteria(max_length=10      )

       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      int							=    self._get_tensors(5      )
       self.assertFalse(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )

       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    self._get_tensors(9      )
       self.assertFalse(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )

       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Any							=    self._get_tensors(10      )
       self.assertTrue(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE       :      str							=    MaxNewTokensCriteria(start_length=5					, max_new_tokens=5      )

       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      int							=    self._get_tensors(5      )
       self.assertFalse(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )

       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Tuple							=    self._get_tensors(9      )
       self.assertFalse(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )

       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Dict							=    self._get_tensors(10      )
       self.assertTrue(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )

       SCREAMING_SNAKE_CASE       :      Dict							=    StoppingCriteriaList([criteria]      )
       self.assertEqual(criteria_list.max_length					, 10      )


     def 				_A       (				self       :	str      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[Any]							=    self._get_tensors(5      )

       SCREAMING_SNAKE_CASE       :      Dict							=    MaxTimeCriteria(max_time=0.1      )
       self.assertFalse(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    MaxTimeCriteria(max_time=0.1					, initial_timestamp=time.time() - 0.2      )
       self.assertTrue(criteria(UpperCAmelCase_					, UpperCAmelCase_      )      )






     def 				_A       (				self       :	Dict      ):
       validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10      )]      )					, 10      )

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

       SCREAMING_SNAKE_CASE       :      Dict							=    validate_stopping_criteria(StoppingCriteriaList()					, 11      )

       self.assertEqual(len(UpperCAmelCase_      )					, 1      )














from typing import TYPE_CHECKING

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


snake_case    				=		{
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TrajectoryTransformerModel""",
       """TrajectoryTransformerPreTrainedModel""",
       """load_tf_weights_in_trajectory_transformer""",
   ]


if TYPE_CHECKING:
   from .configuration_trajectory_transformer import (
       TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TrajectoryTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_trajectory_transformer import (
          TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TrajectoryTransformerModel,
          TrajectoryTransformerPreTrainedModel,
          load_tf_weights_in_trajectory_transformer,
      )


else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






