





'''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


__snake_case:  Any								=			"sshleifer/bart-tiny-random"
__snake_case:  str								=			"patrickvonplaten/t5-tiny-random"


@require_torch
class  _UpperCAmelCase ( unittest.TestCase    ):

     """simple docstring"""

     @cached_property
     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       return AutoConfig.from_pretrained(lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	str      =							create_student_by_copying_alternating_layers(lowerCAmelCase_							,		tempfile.mkdtemp()							,		e=1							,		d=1 )
       self.assertEqual(student.config.num_hidden_layers							,		1 )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	List[Any]      =							create_student_by_copying_alternating_layers(lowerCAmelCase_							,		tempfile.mkdtemp()							,		e=1							,		d=lowerCAmelCase_ )


     def 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	int      =							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 							_lowerCAmelCase  (      self ):

       '''simple docstring'''

       a_      :	Dict      =							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 							_lowerCAmelCase  (      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_flax_available,
    is_tf_available,
    is_torch_available,
)


__snake_case:  Dict								=			{"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["EncoderDecoderModel"]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  Any								=			["TFEncoderDecoderModel"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["FlaxEncoderDecoderModel"]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

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


