


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			:			str        =     '''sshleifer/bart-tiny-random'''
SCREAMING_SNAKE_CASE			:			Union[str, Any]        =     '''patrickvonplaten/t5-tiny-random'''





@require_torch
class        snake_case__	(			unittest.TestCase							):





	@cached_property
	def      A					(	self				)		->      Optional[int]:
								"""simple docstring"""






								return AutoConfig.from_pretrained(lowercase__				)





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






								a_		,					*a_							: List[Any]								=						create_student_by_copying_alternating_layers(lowercase__					,     tempfile.mkdtemp()					,     e=1					,     d=1				)
								self.assertEqual(student.config.num_hidden_layers					,     1				)





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






								a_		,					*a_							: str								=						create_student_by_copying_alternating_layers(lowercase__					,     tempfile.mkdtemp()					,     e=1					,     d=lowercase__				)





	def      A					(	self				)		->      Tuple:
								"""simple docstring"""






								a_		,					*a_							: Union[str, Any]								=						create_student_by_copying_alternating_layers(lowercase__					,     tempfile.mkdtemp()					,     e=1					,     d=lowercase__				)
								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_							: List[Any]								=						create_student_by_copying_alternating_layers(lowercase__					,     tempfile.mkdtemp()					,     e=1					,     d=1				)
								self.assertEqual(student.config.encoder_layers					,     1				)
								self.assertEqual(student.config.decoder_layers					,     1				)





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






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



from typing import TYPE_CHECKING

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


_lowercase:	Optional[Any]       			=		{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''RagModel''',
				    '''RagPreTrainedModel''',
				    '''RagSequenceForGeneration''',
				    '''RagTokenForGeneration''',
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase:	Optional[Any]       			=		[
				    '''TFRagModel''',
				    '''TFRagPreTrainedModel''',
				    '''TFRagSequenceForGeneration''',
				    '''TFRagTokenForGeneration''',
				]


if TYPE_CHECKING:
				from .configuration_rag import RagConfig
				from .retrieval_rag import RagRetriever
				from .tokenization_rag import RagTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_rag import (
								    TFRagModel,
								    TFRagPreTrainedModel,
								    TFRagSequenceForGeneration,
								    TFRagTokenForGeneration,
								)

else:
				import sys

				_lowercase:	Optional[int]       			=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






