





'''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   UpperCamelCase__      (				self    :					List[Any]							,	UpperCamelCase__    :					Dict  )     ->						Optional[Any]:
    _UpperCamelCase       			=3
    _UpperCamelCase       			=250

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


  def   UpperCamelCase__      (				self    :					List[str]  )     ->						Optional[Any]:
    _UpperCamelCase       			=self._get_tensors(5  )

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

    self.assertFalse(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )

    _UpperCamelCase       			=self._get_tensors(9  )
    self.assertFalse(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )

    _UpperCamelCase       			=self._get_tensors(10  )
    self.assertTrue(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )


  def   UpperCamelCase__      (				self    :					Union[str, Any]  )     ->						List[Any]:
    _UpperCamelCase       			=MaxLengthCriteria(max_length=10  )

    _UpperCamelCase       			=self._get_tensors(5  )
    self.assertFalse(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )

    _UpperCamelCase       			=self._get_tensors(9  )
    self.assertFalse(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )

    _UpperCamelCase       			=self._get_tensors(10  )
    self.assertTrue(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )


  def   UpperCamelCase__      (				self    :					int  )     ->						Optional[Any]:
    _UpperCamelCase       			=MaxNewTokensCriteria(start_length=5							,	max_new_tokens=5  )

    _UpperCamelCase       			=self._get_tensors(5  )
    self.assertFalse(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )

    _UpperCamelCase       			=self._get_tensors(9  )
    self.assertFalse(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )

    _UpperCamelCase       			=self._get_tensors(10  )
    self.assertTrue(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )

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


  def   UpperCamelCase__      (				self    :					str  )     ->						int:
    _UpperCamelCase       			=self._get_tensors(5  )

    _UpperCamelCase       			=MaxTimeCriteria(max_time=0.1  )
    self.assertFalse(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )

    _UpperCamelCase       			=MaxTimeCriteria(max_time=0.1							,	initial_timestamp=time.time() - 0.2  )
    self.assertTrue(criteria(UpperCamelCase__							,	UpperCamelCase__  )  )




  def   UpperCamelCase__      (				self    :					str  )     ->						Dict:
    validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10  )]  )							,	10  )

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

    _UpperCamelCase       			=validate_stopping_criteria(StoppingCriteriaList()							,	11  )

    self.assertEqual(len(UpperCamelCase__  )							,	1  )













'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase						: str		=   {
    'configuration_speech_to_text': ['SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Speech2TextConfig'],
    'processing_speech_to_text': ['Speech2TextProcessor'],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: List[str]		=   ['Speech2TextTokenizer']

try:
      if not is_speech_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: str		=   ['Speech2TextFeatureExtractor']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: Optional[Any]		=   [
          'TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'TFSpeech2TextForConditionalGeneration',
          'TFSpeech2TextModel',
          'TFSpeech2TextPreTrainedModel',
      ]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __lowerCamelCase						: Union[str, Any]		=   [
          'SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
          'Speech2TextForConditionalGeneration',
          'Speech2TextModel',
          'Speech2TextPreTrainedModel',
      ]


if TYPE_CHECKING:
      from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
      from .processing_speech_to_text import SpeechaTextProcessor

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_speech_to_text import SpeechaTextTokenizer

      try:
            if not is_speech_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_speech_to_text import (
                TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFSpeechaTextForConditionalGeneration,
                TFSpeechaTextModel,
                TFSpeechaTextPreTrainedModel,
            )

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_speech_to_text import (
                SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SpeechaTextForConditionalGeneration,
                SpeechaTextModel,
                SpeechaTextPreTrainedModel,
            )

else:
      import sys

      __lowerCamelCase						: Optional[int]		=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






