



"""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       ):



  def 	lowerCamelCase__       (						self :					List[Any]       ,		lowerCAmelCase :					Dict		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=3
     SCREAMING_SNAKE_CASE_: List[str]     			=250

     SCREAMING_SNAKE_CASE_: Any     			=ids_tensor((batch_size, length)       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Tuple     			=torch.ones((batch_size, length)       ,		device=lowerCAmelCase       ,		dtype=torch.float		) / length
     return input_ids, scores



  def 	lowerCamelCase__       (						self :					List[str]		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=self._get_tensors(5		)

     SCREAMING_SNAKE_CASE_: int     			=StoppingCriteriaList(
         [
             MaxLengthCriteria(max_length=10		),
             MaxTimeCriteria(max_time=0.1		),
         ]		)

     self.assertFalse(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)

     SCREAMING_SNAKE_CASE_: Dict     			=self._get_tensors(9		)
     self.assertFalse(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)

     SCREAMING_SNAKE_CASE_: int     			=self._get_tensors(10		)
     self.assertTrue(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)



  def 	lowerCamelCase__       (						self :					Union[str, Any]		)     ->					List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: int     			=MaxLengthCriteria(max_length=10		)

     SCREAMING_SNAKE_CASE_: Dict     			=self._get_tensors(5		)
     self.assertFalse(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)

     SCREAMING_SNAKE_CASE_: Tuple     			=self._get_tensors(9		)
     self.assertFalse(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)

     SCREAMING_SNAKE_CASE_: int     			=self._get_tensors(10		)
     self.assertTrue(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)



  def 	lowerCamelCase__       (						self :					int		)     ->					Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: Optional[int]     			=MaxNewTokensCriteria(start_length=5       ,		max_new_tokens=5		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self._get_tensors(5		)
     self.assertFalse(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)

     SCREAMING_SNAKE_CASE_: Any     			=self._get_tensors(9		)
     self.assertFalse(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)

     SCREAMING_SNAKE_CASE_: str     			=self._get_tensors(10		)
     self.assertTrue(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)

     SCREAMING_SNAKE_CASE_: List[str]     			=StoppingCriteriaList([criteria]		)
     self.assertEqual(criteria_list.max_length       ,		10		)



  def 	lowerCamelCase__       (						self :					str		)     ->					int:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_: List[Any]     			=self._get_tensors(5		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=MaxTimeCriteria(max_time=0.1		)
     self.assertFalse(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)

     SCREAMING_SNAKE_CASE_: Optional[int]     			=MaxTimeCriteria(max_time=0.1       ,		initial_timestamp=time.time() - 0.2		)
     self.assertTrue(criteria(lowerCAmelCase       ,		lowerCAmelCase		)		)





  def 	lowerCamelCase__       (						self :					str		)     ->					Dict:


     '''simple docstring'''




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

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

     SCREAMING_SNAKE_CASE_: Optional[int]     			=validate_stopping_criteria(StoppingCriteriaList()       ,		11		)

     self.assertEqual(len(lowerCAmelCase		)       ,		1		)








"""simple docstring"""
from typing import TYPE_CHECKING

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


_UpperCAmelCase     =			{
    """configuration_xlm""": ["""XLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLMConfig""", """XLMOnnxConfig"""],
    """tokenization_xlm""": ["""XLMTokenizer"""],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """XLMForMultipleChoice""",
       """XLMForQuestionAnswering""",
       """XLMForQuestionAnsweringSimple""",
       """XLMForSequenceClassification""",
       """XLMForTokenClassification""",
       """XLMModel""",
       """XLMPreTrainedModel""",
       """XLMWithLMHeadModel""",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TFXLMForMultipleChoice""",
       """TFXLMForQuestionAnsweringSimple""",
       """TFXLMForSequenceClassification""",
       """TFXLMForTokenClassification""",
       """TFXLMMainLayer""",
       """TFXLMModel""",
       """TFXLMPreTrainedModel""",
       """TFXLMWithLMHeadModel""",
   ]


if TYPE_CHECKING:
   from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
   from .tokenization_xlm import XLMTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_xlm import (
          XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          XLMForMultipleChoice,
          XLMForQuestionAnswering,
          XLMForQuestionAnsweringSimple,
          XLMForSequenceClassification,
          XLMForTokenClassification,
          XLMModel,
          XLMPreTrainedModel,
          XLMWithLMHeadModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_xlm import (
          TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFXLMForMultipleChoice,
          TFXLMForQuestionAnsweringSimple,
          TFXLMForSequenceClassification,
          TFXLMForTokenClassification,
          TFXLMMainLayer,
          TFXLMModel,
          TFXLMPreTrainedModel,
          TFXLMWithLMHeadModel,
      )

else:
   import sys

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



