


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 	snake_case_  (	self  ,       __A    ):
        __a					=  3
        __a					=  250

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


     def 	snake_case_  (	self    ):
        __a	,    __a					=  self._get_tensors(5    )

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

        self.assertFalse(criteria(__A  ,       __A    )    )

        __a	,    __a					=  self._get_tensors(9    )
        self.assertFalse(criteria(__A  ,       __A    )    )

        __a	,    __a					=  self._get_tensors(10    )
        self.assertTrue(criteria(__A  ,       __A    )    )


     def 	snake_case_  (	self    ):
        __a					=  MaxLengthCriteria(max_length=10    )

        __a	,    __a					=  self._get_tensors(5    )
        self.assertFalse(criteria(__A  ,       __A    )    )

        __a	,    __a					=  self._get_tensors(9    )
        self.assertFalse(criteria(__A  ,       __A    )    )

        __a	,    __a					=  self._get_tensors(10    )
        self.assertTrue(criteria(__A  ,       __A    )    )


     def 	snake_case_  (	self    ):
        __a					=  MaxNewTokensCriteria(start_length=5  ,       max_new_tokens=5    )

        __a	,    __a					=  self._get_tensors(5    )
        self.assertFalse(criteria(__A  ,       __A    )    )

        __a	,    __a					=  self._get_tensors(9    )
        self.assertFalse(criteria(__A  ,       __A    )    )

        __a	,    __a					=  self._get_tensors(10    )
        self.assertTrue(criteria(__A  ,       __A    )    )

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


     def 	snake_case_  (	self    ):
        __a	,    __a					=  self._get_tensors(5    )

        __a					=  MaxTimeCriteria(max_time=0.1    )
        self.assertFalse(criteria(__A  ,       __A    )    )

        __a					=  MaxTimeCriteria(max_time=0.1  ,       initial_timestamp=time.time() - 0.2    )
        self.assertTrue(criteria(__A  ,       __A    )    )


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

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

        __a					=  validate_stopping_criteria(StoppingCriteriaList()  ,       11    )

        self.assertEqual(len(__A    )  ,       1    )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE							    =							{'configuration_wavlm': ['WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'WavLMConfig']}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST',
      'WavLMForAudioFrameClassification',
      'WavLMForCTC',
      'WavLMForSequenceClassification',
      'WavLMForXVector',
      'WavLMModel',
      'WavLMPreTrainedModel',
  ]

if TYPE_CHECKING:
  from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_wavlm import (
        WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
        WavLMForAudioFrameClassification,
        WavLMForCTC,
        WavLMForSequenceClassification,
        WavLMForXVector,
        WavLMModel,
        WavLMPreTrainedModel,
    )

else:
  import sys

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



