


'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase    :    Tuple			     =  {
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    Optional[int]			     =  [
	    """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """AltCLIPPreTrainedModel""",
	    """AltCLIPModel""",
	    """AltCLIPTextModel""",
	    """AltCLIPVisionModel""",
	]


if TYPE_CHECKING:
	from .configuration_altclip import (
	    ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    AltCLIPConfig,
	    AltCLIPTextConfig,
	    AltCLIPVisionConfig,
	)
	from .processing_altclip import AltCLIPProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_altclip import (
		    ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    AltCLIPModel,
		    AltCLIPPreTrainedModel,
		    AltCLIPTextModel,
		    AltCLIPVisionModel,
		)


else:
	import sys

	_lowercase    :    Union[str, Any]			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









'''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 ):




				def   a__(	self    :				List[Any]   ,					lowerCAmelCase    :				Optional[Any]   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase         =			3
										UpperCAmelCase         =			250

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




				def   a__(	self    :				Dict   )->  Optional[int]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )

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

										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(9   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(10   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )




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

										"""simple docstring"""




										UpperCAmelCase         =			MaxLengthCriteria(max_length=10   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(9   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(10   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )




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

										"""simple docstring"""




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

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(9   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(10   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

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




				def   a__(	self    :				Tuple   )->  Optional[Any]:

										"""simple docstring"""




										UpperCAmelCase       ,       UpperCAmelCase         =			self._get_tensors(5   )

										UpperCAmelCase         =			MaxTimeCriteria(max_time=0.1   )
										self.assertFalse(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )

										UpperCAmelCase         =			MaxTimeCriteria(max_time=0.1   ,					initial_timestamp=time.time() - 0.2   )
										self.assertTrue(criteria(lowerCAmelCase   ,					lowerCAmelCase   )   )





				def   a__(	self    :				int   )->  Any:

										"""simple docstring"""




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

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

										UpperCAmelCase         =			validate_stopping_criteria(StoppingCriteriaList()   ,					11   )

										self.assertEqual(len(lowerCAmelCase   )   ,					1   )





