"""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					):




    """simple docstring"""




    def  lowercase					(		self							:     Any	,      A_							:     Any    )    ->       Optional[Any]:
         __snake_case				=							3
         __snake_case				=							250

         __snake_case				=							ids_tensor((batch_size, length)	,      lowerCamelCase_    )
         __snake_case				=							torch.ones((batch_size, length)	,      device=lowerCamelCase_	,      dtype=torch.float    ) / length
         return input_ids, scores
    def  lowercase					(		self							:     Any    )    ->       Union[str, Any]:
         __snake_case				=							self._get_tensors(5    )

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

         self.assertFalse(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )

         __snake_case				=							self._get_tensors(9    )
         self.assertFalse(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )

         __snake_case				=							self._get_tensors(10    )
         self.assertTrue(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )
    def  lowercase					(		self							:     str    )    ->       Any:
         __snake_case				=							MaxLengthCriteria(max_length=10    )

         __snake_case				=							self._get_tensors(5    )
         self.assertFalse(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )

         __snake_case				=							self._get_tensors(9    )
         self.assertFalse(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )

         __snake_case				=							self._get_tensors(10    )
         self.assertTrue(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )
    def  lowercase					(		self							:     List[str]    )    ->       int:
         __snake_case				=							MaxNewTokensCriteria(start_length=5	,      max_new_tokens=5    )

         __snake_case				=							self._get_tensors(5    )
         self.assertFalse(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )

         __snake_case				=							self._get_tensors(9    )
         self.assertFalse(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )

         __snake_case				=							self._get_tensors(10    )
         self.assertTrue(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )

         __snake_case				=							StoppingCriteriaList([criteria]    )
         self.assertEqual(criteria_list.max_length	,      10    )
    def  lowercase					(		self							:     Optional[int]    )    ->       Any:
         __snake_case				=							self._get_tensors(5    )

         __snake_case				=							MaxTimeCriteria(max_time=0.1    )
         self.assertFalse(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )

         __snake_case				=							MaxTimeCriteria(max_time=0.1	,      initial_timestamp=time.time() - 0.2    )
         self.assertTrue(criteria(lowerCamelCase_	,      lowerCamelCase_    )    )







    def  lowercase					(		self							:     List[Any]    )    ->       Optional[int]:
         validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10    )]    )	,      10    )

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

         __snake_case				=							validate_stopping_criteria(StoppingCriteriaList()	,      11    )

         self.assertEqual(len(lowerCamelCase_    )	,      1    )
"""simple docstring"""



from typing import TYPE_CHECKING

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


__lowercase		:		Optional[Any]														=							{
    "configuration_falcon": ["FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP", "FalconConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowercase		:		str														=							[
           "FALCON_PRETRAINED_MODEL_ARCHIVE_LIST",
           "FalconForCausalLM",
           "FalconModel",
           "FalconPreTrainedModel",
           "FalconForSequenceClassification",
           "FalconForTokenClassification",
           "FalconForQuestionAnswering",
       ]


if TYPE_CHECKING:
       from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_falcon import (
                  FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
                  FalconForCausalLM,
                  FalconForQuestionAnswering,
                  FalconForSequenceClassification,
                  FalconForTokenClassification,
                  FalconModel,
                  FalconPreTrainedModel,
              )


else:
       import sys

       __lowercase		:		List[Any]														=							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)