

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


_lowerCamelCase      :					List[Any]					    =      '''sshleifer/bart-tiny-random'''
_lowerCamelCase      :					Any					    =      '''patrickvonplaten/t5-tiny-random'''





@require_torch
class        lowerCAmelCase__  (					unittest.TestCase			):



       '''simple docstring'''







       @cached_property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return AutoConfig.from_pretrained(__lowerCAmelCase     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ,		*__A    =create_student_by_copying_alternating_layers(__lowerCAmelCase ,   tempfile.mkdtemp() ,   e=1 ,   d=__lowerCAmelCase     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ,		*__A    =create_student_by_copying_alternating_layers(__lowerCAmelCase ,   tempfile.mkdtemp() ,   e=1 ,   d=__lowerCAmelCase     )
             self.assertEqual(student.config.encoder_layers ,   1     )
             self.assertEqual(student.config.decoder_layers ,   self.teacher_config.encoder_layers     )

       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             __A    ,		*__A    =create_student_by_copying_alternating_layers(__lowerCAmelCase ,   tempfile.mkdtemp() ,   e=1 ,   d=1     )
             self.assertEqual(student.config.encoder_layers ,   1     )
             self.assertEqual(student.config.decoder_layers ,   1     )


       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



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








'''simple docstring'''


from typing import TYPE_CHECKING

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


_lowercase  =			{
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase  =			[
	    """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """BioGptForCausalLM""",
	    """BioGptForTokenClassification""",
	    """BioGptForSequenceClassification""",
	    """BioGptModel""",
	    """BioGptPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
	from .tokenization_biogpt import BioGptTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_biogpt import (
		    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BioGptForCausalLM,
		    BioGptForSequenceClassification,
		    BioGptForTokenClassification,
		    BioGptModel,
		    BioGptPreTrainedModel,
		)


else:
	import sys

	_lowercase  =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

