


'''simple docstring'''

import tempfile
import unittest

from make_student import create_student_by_copying_alternating_layers

from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch


a_						:       Tuple      			=				'''sshleifer/bart-tiny-random'''
a_						:       Union[str, Any]      			=				'''patrickvonplaten/t5-tiny-random'''





@require_torch
class   snake_case					(					unittest.TestCase    ):





		"""simple docstring"""




		@cached_property
		def 						snake_case	(					self  ):







			"""simple docstring"""
			return AutoConfig.from_pretrained(UpperCamelCase  )






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_				,*lowerCamelCase_  				=						create_student_by_copying_alternating_layers(UpperCamelCase    ,						tempfile.mkdtemp()    ,						e=1    ,						d=1  )
			self.assertEqual(student.config.num_hidden_layers    ,						1  )






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_				,*lowerCamelCase_  				=						create_student_by_copying_alternating_layers(UpperCamelCase    ,						tempfile.mkdtemp()    ,						e=1    ,						d=UpperCamelCase  )






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_				,*lowerCamelCase_  				=						create_student_by_copying_alternating_layers(UpperCamelCase    ,						tempfile.mkdtemp()    ,						e=1    ,						d=UpperCamelCase  )
			self.assertEqual(student.config.encoder_layers    ,						1  )
			self.assertEqual(student.config.decoder_layers    ,						self.teacher_config.encoder_layers  )






		def 						snake_case	(					self  ):







			"""simple docstring"""
			lowerCamelCase_				,*lowerCamelCase_  				=						create_student_by_copying_alternating_layers(UpperCamelCase    ,						tempfile.mkdtemp()    ,						e=1    ,						d=1  )
			self.assertEqual(student.config.encoder_layers    ,						1  )
			self.assertEqual(student.config.decoder_layers    ,						1  )







		def 						snake_case	(					self  ):







			"""simple docstring"""
			with self.assertRaises(UpperCamelCase  ):
				create_student_by_copying_alternating_layers(UpperCamelCase    ,						tempfile.mkdtemp()    ,						e=UpperCamelCase    ,						d=UpperCamelCase  )







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


a_						:       List[Any]      			=				{
    """configuration_owlvit""": [
        """OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """OwlViTConfig""",
        """OwlViTOnnxConfig""",
        """OwlViTTextConfig""",
        """OwlViTVisionConfig""",
    ],
    """processing_owlvit""": ["""OwlViTProcessor"""],
}


try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:       int      			=				["""OwlViTFeatureExtractor"""]
     a_						:       Any      			=				["""OwlViTImageProcessor"""]

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     a_						:       Tuple      			=				[
         """OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """OwlViTModel""",
         """OwlViTPreTrainedModel""",
         """OwlViTTextModel""",
         """OwlViTVisionModel""",
         """OwlViTForObjectDetection""",
     ]

if TYPE_CHECKING:
     from .configuration_owlvit import (
         OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
         OwlViTConfig,
         OwlViTOnnxConfig,
         OwlViTTextConfig,
         OwlViTVisionConfig,
     )
     from .processing_owlvit import OwlViTProcessor

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_owlvit import OwlViTFeatureExtractor
          from .image_processing_owlvit import OwlViTImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_owlvit import (
              OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
              OwlViTForObjectDetection,
              OwlViTModel,
              OwlViTPreTrainedModel,
              OwlViTTextModel,
              OwlViTVisionModel,
          )

else:
     import sys

     a_						:       Tuple      			=				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



