


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class      __lowerCamelCase       (unittest.TestCase		):







       @slow
       def 						snake_case_     (       self:      Tuple ):






            '''simple docstring'''




            __UpperCamelCase      =       AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small',return_dict=SCREAMING_SNAKE_CASE_ ).to(SCREAMING_SNAKE_CASE_ )
            __UpperCamelCase      =       AutoTokenizer.from_pretrained('google/mt5-small' )

            __UpperCamelCase      =       tokenizer('Hello there',return_tensors='pt' ).input_ids
            __UpperCamelCase      =       tokenizer('Hi I am',return_tensors='pt' ).input_ids

            __UpperCamelCase      =       model(input_ids.to(SCREAMING_SNAKE_CASE_ ),labels=labels.to(SCREAMING_SNAKE_CASE_ ) ).loss
            __UpperCamelCase      =       -(labels.shape[-1] * loss.item())

            __UpperCamelCase      =       -8_4.9_1_2_7
            self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1E-4 )




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


__a  			=					'sshleifer/bart-tiny-random'
__a  			=					'patrickvonplaten/t5-tiny-random'
@require_torch
class  lowercase__(				unittest.TestCase							):



	"""simple docstring"""




	@cached_property
	def 						_lowercase  (       self  :			Any       )       ->     Tuple:
					return AutoConfig.from_pretrained(SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			int       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=1       )
					self.assertEqual(student.config.num_hidden_layers       ,							1       )
	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=SCREAMING_SNAKE_CASE_       )
	def 						_lowercase  (       self  :			List[str]       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=SCREAMING_SNAKE_CASE_       )
					self.assertEqual(student.config.encoder_layers       ,							1       )
					self.assertEqual(student.config.decoder_layers       ,							self.teacher_config.encoder_layers       )
	def 						_lowercase  (       self  :			List[Any]       )       ->     List[Any]:
					lowercase_  ,	*lowercase_       =					create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=1       ,							d=1       )
					self.assertEqual(student.config.encoder_layers       ,							1       )
					self.assertEqual(student.config.decoder_layers       ,							1       )




	def 						_lowercase  (       self  :			Union[str, Any]       )       ->     Optional[int]:
					with self.assertRaises(SCREAMING_SNAKE_CASE_       ):
									create_student_by_copying_alternating_layers(SCREAMING_SNAKE_CASE_       ,							tempfile.mkdtemp()       ,							e=SCREAMING_SNAKE_CASE_       ,							d=SCREAMING_SNAKE_CASE_       )




