





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						{"""configuration_beit""": ["""BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BeitConfig""", """BeitOnnxConfig"""]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Dict			      =						["""BeitFeatureExtractor"""]
  SCREAMING_SNAKE_CASE__       :			str			      =						["""BeitImageProcessor"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[int]			      =						[
      """BEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """BeitForImageClassification""",
      """BeitForMaskedImageModeling""",
      """BeitForSemanticSegmentation""",
      """BeitModel""",
      """BeitPreTrainedModel""",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE__       :			Optional[Any]			      =						[
      """FlaxBeitForImageClassification""",
      """FlaxBeitForMaskedImageModeling""",
      """FlaxBeitModel""",
      """FlaxBeitPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_beit import BeitFeatureExtractor
    from .image_processing_beit import BeitImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_beit import (
        BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
        BeitForImageClassification,
        BeitForMaskedImageModeling,
        BeitForSemanticSegmentation,
        BeitModel,
        BeitPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_beit import (
        FlaxBeitForImageClassification,
        FlaxBeitForMaskedImageModeling,
        FlaxBeitModel,
        FlaxBeitPreTrainedModel,
    )


else:
  import sys

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








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


SCREAMING_SNAKE_CASE__       :			List[Any]			      =						"""sshleifer/bart-tiny-random"""
SCREAMING_SNAKE_CASE__       :			Tuple			      =						"""patrickvonplaten/t5-tiny-random"""







@require_torch
class   __lowerCAmelCase			(					unittest.TestCase  ):





   @cached_property
   def 	_snake_case ( self      )				->  List[Any]:
      """simple docstring"""





      return AutoConfig.from_pretrained(snake_case      )





   def 	_snake_case ( self      )				->  Any:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.num_hidden_layers					,			1      )





   def 	_snake_case ( self      )				->  str:
      """simple docstring"""





      a__		,     *a__	:						int      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )





   def 	_snake_case ( self      )				->  List[str]:
      """simple docstring"""





      a__		,     *a__	:						Any      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=snake_case      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			self.teacher_config.encoder_layers      )





   def 	_snake_case ( self      )				->  Optional[int]:
      """simple docstring"""





      a__		,     *a__	:						List[str]      		=  create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=1					,			d=1      )
      self.assertEqual(student.config.encoder_layers					,			1      )
      self.assertEqual(student.config.decoder_layers					,			1      )





   def 	_snake_case ( self      )				->  int:
      """simple docstring"""





      with self.assertRaises(snake_case      ):
         create_student_by_copying_alternating_layers(snake_case					,			tempfile.mkdtemp()					,			e=snake_case					,			d=snake_case      )

