


"""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 	__snake_case							(					self				:    Optional[Any]				, __UpperCAmelCase				:    str):
      a						:		Optional[int]      							=			3
      a						:		List[Any]      							=			250

      a						:		Optional[Any]      							=			ids_tensor((batch_size, length)				, __UpperCAmelCase)
      a						:		List[Any]      							=			torch.ones((batch_size, length)				, device=__UpperCAmelCase				, dtype=torch.float) / length
      return input_ids, scores






    def 	__snake_case							(					self				:    Union[str, Any]):
      a							,		a						:		List[Any]      							=			self._get_tensors(5)

      a						:		Union[str, Any]      							=			StoppingCriteriaList(
          [
              MaxLengthCriteria(max_length=10),
              MaxTimeCriteria(max_time=0.1),
          ])

      self.assertFalse(criteria(__UpperCAmelCase				, __UpperCAmelCase))

      a							,		a						:		Any      							=			self._get_tensors(9)
      self.assertFalse(criteria(__UpperCAmelCase				, __UpperCAmelCase))

      a							,		a						:		Any      							=			self._get_tensors(10)
      self.assertTrue(criteria(__UpperCAmelCase				, __UpperCAmelCase))






    def 	__snake_case							(					self				:    Union[str, Any]):
      a						:		Any      							=			MaxLengthCriteria(max_length=10)

      a							,		a						:		Optional[Any]      							=			self._get_tensors(5)
      self.assertFalse(criteria(__UpperCAmelCase				, __UpperCAmelCase))

      a							,		a						:		List[Any]      							=			self._get_tensors(9)
      self.assertFalse(criteria(__UpperCAmelCase				, __UpperCAmelCase))

      a							,		a						:		List[Any]      							=			self._get_tensors(10)
      self.assertTrue(criteria(__UpperCAmelCase				, __UpperCAmelCase))






    def 	__snake_case							(					self				:    Optional[Any]):
      a						:		str      							=			MaxNewTokensCriteria(start_length=5				, max_new_tokens=5)

      a							,		a						:		str      							=			self._get_tensors(5)
      self.assertFalse(criteria(__UpperCAmelCase				, __UpperCAmelCase))

      a							,		a						:		str      							=			self._get_tensors(9)
      self.assertFalse(criteria(__UpperCAmelCase				, __UpperCAmelCase))

      a							,		a						:		Dict      							=			self._get_tensors(10)
      self.assertTrue(criteria(__UpperCAmelCase				, __UpperCAmelCase))

      a						:		Dict      							=			StoppingCriteriaList([criteria])
      self.assertEqual(criteria_list.max_length				, 10)






    def 	__snake_case							(					self				:    str):
      a							,		a						:		Dict      							=			self._get_tensors(5)

      a						:		List[str]      							=			MaxTimeCriteria(max_time=0.1)
      self.assertFalse(criteria(__UpperCAmelCase				, __UpperCAmelCase))

      a						:		Dict      							=			MaxTimeCriteria(max_time=0.1				, initial_timestamp=time.time() - 0.2)
      self.assertTrue(criteria(__UpperCAmelCase				, __UpperCAmelCase))






    def 	__snake_case							(					self				:    Union[str, Any]):
      validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10)])				, 10)

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

      a						:		Tuple      							=			validate_stopping_criteria(StoppingCriteriaList()				, 11)

      self.assertEqual(len(__UpperCAmelCase)				, 1)








'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase    :							Any      =					{
    "configuration_chinese_clip": [
        "CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "ChineseCLIPConfig",
        "ChineseCLIPOnnxConfig",
        "ChineseCLIPTextConfig",
        "ChineseCLIPVisionConfig",
    ],
    "processing_chinese_clip": ["ChineseCLIPProcessor"],
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Optional[Any]      =					["ChineseCLIPFeatureExtractor"]
					lowercase    :							List[Any]      =					["ChineseCLIPImageProcessor"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Any      =					[
					    "CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "ChineseCLIPModel",
					    "ChineseCLIPPreTrainedModel",
					    "ChineseCLIPTextModel",
					    "ChineseCLIPVisionModel",
					]

if TYPE_CHECKING:
					from .configuration_chinese_clip import (
					    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ChineseCLIPConfig,
					    ChineseCLIPOnnxConfig,
					    ChineseCLIPTextConfig,
					    ChineseCLIPVisionConfig,
					)
					from .processing_chinese_clip import ChineseCLIPProcessor

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_chinese_clip import (
										    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ChineseCLIPModel,
										    ChineseCLIPPreTrainedModel,
										    ChineseCLIPTextModel,
										    ChineseCLIPVisionModel,
										)

else:
					import sys

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

