

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






@require_torch
class 	__magic_name__    (			unittest.TestCase      ):





			@cached_property
			def 		__magic_name__    (		self					)   -> Optional[Any]:




						'''simple docstring'''
						return AutoConfig.from_pretrained(__snake_case					)





			def 		__magic_name__    (		self					)   -> Tuple:




						'''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 		__magic_name__    (		self					)   -> str:




						'''simple docstring'''
						__a      ,    *__a        =create_student_by_copying_alternating_layers(__snake_case					,  tempfile.mkdtemp()					,  e=1					,  d=__snake_case					)





			def 		__magic_name__    (		self					)   -> str:




						'''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 		__magic_name__    (		self					)   -> List[Any]:




						'''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 		__magic_name__    (		self					)   -> int:




						'''simple docstring'''
						with self.assertRaises(__snake_case					):
									create_student_by_copying_alternating_layers(__snake_case					,  tempfile.mkdtemp()					,  e=__snake_case					,  d=__snake_case					)









from ... import PretrainedConfig


_lowerCAmelCase					:			Any  				=			{
    "sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
			SCREAMING_SNAKE_CASE							   =						'nezha'





			def __init__(		self					,  __snake_case=2_1128					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.1					,  __snake_case=0.1					,  __snake_case=512					,  __snake_case=64					,  __snake_case=2					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=0.1					,  __snake_case=0					,  __snake_case=2					,  __snake_case=3					,  __snake_case=True					,  **__snake_case					,  )   -> str:




						'''simple docstring'''
						super().__init__(pad_token_id=__snake_case					,  bos_token_id=__snake_case					,  eos_token_id=__snake_case					,  **__snake_case					)

						__a        =vocab_size
						__a        =hidden_size
						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =hidden_act
						__a        =intermediate_size
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =max_position_embeddings
						__a        =max_relative_position
						__a        =type_vocab_size
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =classifier_dropout
						__a        =use_cache






