



'''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 _UpperCamelCase		(  unittest.TestCase		):





				'''simple docstring'''





				def 					__lowerCamelCase  (				self	:	List[str]	,							_lowerCAmelCase	:	List[str]):







											'''simple docstring'''

											__lowercase													=3
											__lowercase													=2_5_0

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

				def 					__lowerCamelCase  (				self	:	List[str]):







											'''simple docstring'''

											__lowercase						,					__lowercase													=self._get_tensors(5)

											__lowercase													=StoppingCriteriaList(
											    [
											        MaxLengthCriteria(max_length=1_0),
											        MaxTimeCriteria(max_time=0.1),
											    ])

											self.assertFalse(criteria(lowercase_	,							lowercase_))

											__lowercase						,					__lowercase													=self._get_tensors(9)
											self.assertFalse(criteria(lowercase_	,							lowercase_))

											__lowercase						,					__lowercase													=self._get_tensors(1_0)
											self.assertTrue(criteria(lowercase_	,							lowercase_))

				def 					__lowerCamelCase  (				self	:	Optional[int]):







											'''simple docstring'''

											__lowercase													=MaxLengthCriteria(max_length=1_0)

											__lowercase						,					__lowercase													=self._get_tensors(5)
											self.assertFalse(criteria(lowercase_	,							lowercase_))

											__lowercase						,					__lowercase													=self._get_tensors(9)
											self.assertFalse(criteria(lowercase_	,							lowercase_))

											__lowercase						,					__lowercase													=self._get_tensors(1_0)
											self.assertTrue(criteria(lowercase_	,							lowercase_))

				def 					__lowerCamelCase  (				self	:	Dict):







											'''simple docstring'''

											__lowercase													=MaxNewTokensCriteria(start_length=5	,							max_new_tokens=5)

											__lowercase						,					__lowercase													=self._get_tensors(5)
											self.assertFalse(criteria(lowercase_	,							lowercase_))

											__lowercase						,					__lowercase													=self._get_tensors(9)
											self.assertFalse(criteria(lowercase_	,							lowercase_))

											__lowercase						,					__lowercase													=self._get_tensors(1_0)
											self.assertTrue(criteria(lowercase_	,							lowercase_))

											__lowercase													=StoppingCriteriaList([criteria])
											self.assertEqual(criteria_list.max_length	,							1_0)

				def 					__lowerCamelCase  (				self	:	List[str]):







											'''simple docstring'''

											__lowercase						,					__lowercase													=self._get_tensors(5)

											__lowercase													=MaxTimeCriteria(max_time=0.1)
											self.assertFalse(criteria(lowercase_	,							lowercase_))

											__lowercase													=MaxTimeCriteria(max_time=0.1	,							initial_timestamp=time.time() - 0.2)
											self.assertTrue(criteria(lowercase_	,							lowercase_))

				def 					__lowerCamelCase  (				self	:	Tuple):







											'''simple docstring'''

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

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

											__lowercase													=validate_stopping_criteria(StoppingCriteriaList()	,							1_1)

											self.assertEqual(len(lowercase_)	,							1)





"""simple docstring"""

from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_            =    {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   pass

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE_            =    [
       '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''RoCBertForCausalLM''',
       '''RoCBertForMaskedLM''',
       '''RoCBertForMultipleChoice''',
       '''RoCBertForPreTraining''',
       '''RoCBertForQuestionAnswering''',
       '''RoCBertForSequenceClassification''',
       '''RoCBertForTokenClassification''',
       '''RoCBertLayer''',
       '''RoCBertModel''',
       '''RoCBertPreTrainedModel''',
       '''load_tf_weights_in_roc_bert''',
   ]

if TYPE_CHECKING:
   from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
   from .tokenization_roc_bert import RoCBertTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      raise OptionalDependencyNotAvailable()

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_roc_bert import (
          ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
          RoCBertForCausalLM,
          RoCBertForMaskedLM,
          RoCBertForMultipleChoice,
          RoCBertForPreTraining,
          RoCBertForQuestionAnswering,
          RoCBertForSequenceClassification,
          RoCBertForTokenClassification,
          RoCBertLayer,
          RoCBertModel,
          RoCBertPreTrainedModel,
          load_tf_weights_in_roc_bert,
      )


else:
   import sys

   SCREAMING_SNAKE_CASE_            =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



