

"""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		  =     """sshleifer/bart-tiny-random"""
__A		  =     """patrickvonplaten/t5-tiny-random"""





@require_torch
class        snake_case      (							unittest.TestCase    ):

				@cached_property
				def 				lowercase_	(      self				:							List[Any])->  str:



						'''simple docstring'''


						return AutoConfig.from_pretrained(UpperCAmelCase__)

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



						'''simple docstring'''


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

				def 				lowercase_	(      self				:							int)->  Any:



						'''simple docstring'''


						__lowerCAmelCase ,					*__lowerCAmelCase:			Union[str, Any]        =     create_student_by_copying_alternating_layers(UpperCAmelCase__	,    tempfile.mkdtemp()	,    e=1	,    d=UpperCAmelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase ,					*__lowerCAmelCase:			List[str]        =     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 				lowercase_	(      self				:							str)->  Optional[int]:



						'''simple docstring'''


						__lowerCAmelCase ,					*__lowerCAmelCase:			Dict        =     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 				lowercase_	(      self				:							List[Any])->  Optional[int]:



						'''simple docstring'''


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








from typing import TYPE_CHECKING

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


_lowerCamelCase      :						Any							=  {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

try:
  if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizer"""]

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						int							=  ["""XLNetTokenizerFast"""]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Union[str, Any]							=  [
      """XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XLNetForMultipleChoice""",
      """XLNetForQuestionAnswering""",
      """XLNetForQuestionAnsweringSimple""",
      """XLNetForSequenceClassification""",
      """XLNetForTokenClassification""",
      """XLNetLMHeadModel""",
      """XLNetModel""",
      """XLNetPreTrainedModel""",
      """load_tf_weights_in_xlnet""",
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase      :						Dict							=  [
      """TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """TFXLNetForMultipleChoice""",
      """TFXLNetForQuestionAnsweringSimple""",
      """TFXLNetForSequenceClassification""",
      """TFXLNetForTokenClassification""",
      """TFXLNetLMHeadModel""",
      """TFXLNetMainLayer""",
      """TFXLNetModel""",
      """TFXLNetPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

  try:
    if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet import XLNetTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_xlnet_fast import XLNetTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlnet import (
        XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLNetForMultipleChoice,
        XLNetForQuestionAnswering,
        XLNetForQuestionAnsweringSimple,
        XLNetForSequenceClassification,
        XLNetForTokenClassification,
        XLNetLMHeadModel,
        XLNetModel,
        XLNetPreTrainedModel,
        load_tf_weights_in_xlnet,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_xlnet import (
        TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFXLNetForMultipleChoice,
        TFXLNetForQuestionAnsweringSimple,
        TFXLNetForSequenceClassification,
        TFXLNetForTokenClassification,
        TFXLNetLMHeadModel,
        TFXLNetMainLayer,
        TFXLNetModel,
        TFXLNetPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase      :						int							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


