



from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE__       =      {
    '''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
    '''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							pass

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							SCREAMING_SNAKE_CASE__       =      [
							    '''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''RoCBertForCausalLM''',
							    '''RoCBertForMaskedLM''',
							    '''RoCBertForMultipleChoice''',
							    '''RoCBertForPreTraining''',
							    '''RoCBertForQuestionAnswering''',
							    '''RoCBertForSequenceClassification''',
							    '''RoCBertForTokenClassification''',
							    '''RoCBertLayer''',
							    '''RoCBertModel''',
							    '''RoCBertPreTrainedModel''',
							    '''load_tf_weights_in_roc_bert''',
							]

if TYPE_CHECKING:
							from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
							from .tokenization_roc_bert import RoCBertTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														raise OptionalDependencyNotAvailable()

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_roc_bert import (
														    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RoCBertForCausalLM,
														    RoCBertForMaskedLM,
														    RoCBertForMultipleChoice,
														    RoCBertForPreTraining,
														    RoCBertForQuestionAnswering,
														    RoCBertForSequenceClassification,
														    RoCBertForTokenClassification,
														    RoCBertLayer,
														    RoCBertModel,
														    RoCBertPreTrainedModel,
														    load_tf_weights_in_roc_bert,
														)


else:
							import sys

							SCREAMING_SNAKE_CASE__       =      _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__       =      '''sshleifer/bart-tiny-random'''
SCREAMING_SNAKE_CASE__       =      '''patrickvonplaten/t5-tiny-random'''


@require_torch
class      __lowerCAmelCase     (		unittest.TestCase      ):
					"""simple docstring"""






					@cached_property
					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								return AutoConfig.from_pretrained(_snake_case					)



					def     _a							(     self					:       Optional[Any]					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=1					)
								self.assertEqual(student.config.num_hidden_layers				,					1					)



					def     _a							(     self					:       Optional[int]					):






								"""simple docstring"""


								A__     , *A__       =    create_student_by_copying_alternating_layers(_snake_case				,					tempfile.mkdtemp()				,					e=1				,					d=_snake_case					)



					def     _a							(     self					:       int					):






								"""simple docstring"""


								A__     , *A__       =    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     _a							(     self					:       str					):






								"""simple docstring"""


								A__     , *A__       =    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     _a							(     self					:       str					):






								"""simple docstring"""


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




