


import os
import tempfile
import unittest

from transformers.models.marian.convert_marian_tatoeba_to_pytorch import DEFAULT_REPO, TatoebaConverter
from transformers.testing_utils import slow
from transformers.utils import cached_property
@unittest.skipUnless(os.path.exists(lowercase__    )		,					'''Tatoeba directory does not exist.'''    )
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    List[Any]:
             a_      :	Optional[Any]							=						tempfile.mkdtemp()
             return TatoebaConverter(save_dir=SCREAMING_SNAKE_CASE__  )
      @slow
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Optional[Any]:
             self.resolver.convert_models(['heb-eng']  )

      @slow
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             a_							,	a_      :	Dict							=						self.resolver.write_model_card('opus-mt-he-en'  ,							dry_run=SCREAMING_SNAKE_CASE__  )
             assert mmeta["long_pair"] == "heb-eng"






import copy
import tempfile
import unittest

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

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
   from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder







def  SCREAMING_SNAKE_CASE_		(    __A				:    int						,					__A				:    Tuple						,					__A				:    int						,					__A				:    Any=None						,					__A				:    str=None						,					__A				:    Any=None						,					__A				:    Optional[int]=None						,					__A				:    str=None						,					)			->	List[str]:

       """simple docstring"""

       if attention_mask is None:
              a_      :	List[Any]							=						input_ids.ne(config.pad_token_id       )
       if decoder_attention_mask is None:
              a_      :	str							=						decoder_input_ids.ne(config.pad_token_id       )
       if head_mask is None:
              a_      :	Any							=						torch.ones(config.encoder_layers						,					config.encoder_attention_heads						,					device=__A       )
       if decoder_head_mask is None:
              a_      :	int							=						torch.ones(config.decoder_layers						,					config.decoder_attention_heads						,					device=__A       )
       if cross_attn_head_mask is None:
              a_      :	Union[str, Any]							=						torch.ones(config.decoder_layers						,					config.decoder_attention_heads						,					device=__A       )
       return {
           "input_ids": input_ids,
           "decoder_input_ids": decoder_input_ids,
           "attention_mask": attention_mask,
           "decoder_attention_mask": attention_mask,
           "head_mask": head_mask,
           "decoder_head_mask": decoder_head_mask,
           "cross_attn_head_mask": cross_attn_head_mask,
       }
class 				SCREAMING_SNAKE_CASE__						:
      def __init__( self       : List[str]  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Dict=1_3  ,							SCREAMING_SNAKE_CASE__       : int=7  ,							SCREAMING_SNAKE_CASE__       : Optional[Any]=True  ,							SCREAMING_SNAKE_CASE__       : Any=False  ,							SCREAMING_SNAKE_CASE__       : int=9_9  ,							SCREAMING_SNAKE_CASE__       : int=1_6  ,							SCREAMING_SNAKE_CASE__       : str=2  ,							SCREAMING_SNAKE_CASE__       : Any=4  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=4  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]="relu"  ,							SCREAMING_SNAKE_CASE__       : Tuple=0.1  ,							SCREAMING_SNAKE_CASE__       : Any=0.1  ,							SCREAMING_SNAKE_CASE__       : Dict=0.0  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=0.0  ,							SCREAMING_SNAKE_CASE__       : Optional[int]=2_0  ,							SCREAMING_SNAKE_CASE__       : List[str]=2  ,							SCREAMING_SNAKE_CASE__       : Union[str, Any]=1  ,							SCREAMING_SNAKE_CASE__       : Tuple=0  ,							)	->    Dict:
             a_      :	Dict							=						parent
             a_      :	List[str]							=						batch_size
             a_      :	Optional[int]							=						seq_length
             a_      :	str							=						is_training
             a_      :	List[str]							=						use_labels
             a_      :	Optional[Any]							=						vocab_size
             a_      :	str							=						hidden_size
             a_      :	Optional[int]							=						num_hidden_layers
             a_      :	Any							=						num_attention_heads
             a_      :	Tuple							=						intermediate_size
             a_      :	int							=						hidden_act
             a_      :	List[Any]							=						hidden_dropout_prob
             a_      :	Tuple							=						attention_probs_dropout_prob
             a_      :	Dict							=						encoder_layerdrop
             a_      :	Union[str, Any]							=						decoder_layerdrop
             a_      :	Tuple							=						max_position_embeddings
             a_      :	Tuple							=						eos_token_id
             a_      :	Optional[int]							=						pad_token_id
             a_      :	Union[str, Any]							=						bos_token_id
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Any:
             a_      :	List[Any]							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )
             a_      :	List[str]							=						self.eos_token_id  # Eos Token
             a_      :	str							=						ids_tensor([self.batch_size, self.seq_length]  ,							self.vocab_size  )

             # we need to clamp the input ids here to avoid having pad token in between
             # this is because for M2M100 the position_ids are prepared such that
             # all pad tokens have pos id = 2 and rest are between 2..seq_length
             # and the seq_length here is seq_length - num_pad_tokens
             # but when using past, there is no way of knowing if the past input ids had
             # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
             # position_ids being off by num_pad_tokens in past input
             a_      :	Optional[int]							=						input_ids.clamp(self.pad_token_id + 1  )
             a_      :	Union[str, Any]							=						decoder_input_ids.clamp(self.pad_token_id + 1  )

             a_      :	Any							=						self.get_config()
             a_      :	List[str]							=						prepare_mam_aaa_inputs_dict(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             return config, inputs_dict
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Any:
             return MaMaaaConfig(
                 vocab_size=self.vocab_size  ,							d_model=self.hidden_size  ,							encoder_layers=self.num_hidden_layers  ,							decoder_layers=self.num_hidden_layers  ,							encoder_attention_heads=self.num_attention_heads  ,							decoder_attention_heads=self.num_attention_heads  ,							encoder_ffn_dim=self.intermediate_size  ,							decoder_ffn_dim=self.intermediate_size  ,							dropout=self.hidden_dropout_prob  ,							attention_dropout=self.attention_probs_dropout_prob  ,							encoder_layerdrop=self.encoder_layerdrop  ,							decoder_layerdrop=self.decoder_layerdrop  ,							max_position_embeddings=self.max_position_embeddings  ,							eos_token_id=self.eos_token_id  ,							bos_token_id=self.bos_token_id  ,							pad_token_id=self.pad_token_id  ,							)
      def  SCREAMING_SNAKE_CASE							( self       : Union[str, Any]  )	->    int:
             a_							,	a_      :	List[Any]							=						self.prepare_config_and_inputs()
             return config, inputs_dict
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  )	->    Tuple:
             a_      :	Tuple							=						MaMaaaModel(config=SCREAMING_SNAKE_CASE__  ).get_decoder().to(SCREAMING_SNAKE_CASE__  ).eval()
             a_      :	List[str]							=						inputs_dict['input_ids']
             a_      :	List[str]							=						inputs_dict['attention_mask']
             a_      :	int							=						inputs_dict['head_mask']

             # first forward pass
             a_      :	List[Any]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							head_mask=SCREAMING_SNAKE_CASE__  ,							use_cache=SCREAMING_SNAKE_CASE__  )

             a_							,	a_      :	Optional[Any]							=						outputs.to_tuple()

             # create hypothetical multiple next token and extent to next_input_ids
             a_      :	Union[str, Any]							=						ids_tensor((self.batch_size, 3)  ,							config.vocab_size  )
             a_      :	Any							=						ids_tensor((self.batch_size, 3)  ,							2  )

             # append to next input_ids and
             a_      :	Tuple							=						torch.cat([input_ids, next_tokens]  ,							dim=-1  )
             a_      :	str							=						torch.cat([attention_mask, next_attn_mask]  ,							dim=-1  )

             a_      :	Optional[int]							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )['last_hidden_state']
             a_      :	int							=						model(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  ,							past_key_values=SCREAMING_SNAKE_CASE__  )[
                 'last_hidden_state'
             ]

             # select random slice
             a_      :	str							=						ids_tensor((1,)  ,							output_from_past.shape[-1]  ).item()
             a_      :	Optional[int]							=						output_from_no_past[:, -3:, random_slice_idx].detach()
             a_      :	Optional[int]							=						output_from_past[:, :, random_slice_idx].detach()

             self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]  )

             # test that outputs are equal for slice
             self.parent.assertTrue(torch.allclose(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  ,							atol=1E-2  )  )

      def  SCREAMING_SNAKE_CASE							( self       : int  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Any  )	->    Any:
             a_      :	int							=						MaMaaaModel(config=SCREAMING_SNAKE_CASE__  ).to(SCREAMING_SNAKE_CASE__  ).eval()
             a_      :	Dict							=						model(**SCREAMING_SNAKE_CASE__  )

             a_      :	str							=						outputs.encoder_last_hidden_state
             a_      :	int							=						outputs.last_hidden_state

             with tempfile.TemporaryDirectory() as tmpdirname:
                    a_      :	Dict							=						model.get_encoder()
                    encoder.save_pretrained(SCREAMING_SNAKE_CASE__  )
                    a_      :	Tuple							=						MaMaaaEncoder.from_pretrained(SCREAMING_SNAKE_CASE__  ).to(SCREAMING_SNAKE_CASE__  )

             a_      :	int							=						encoder(inputs_dict['input_ids']  ,							attention_mask=inputs_dict['attention_mask']  )[
                 0
             ]

             self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3  )

             with tempfile.TemporaryDirectory() as tmpdirname:
                    a_      :	Tuple							=						model.get_decoder()
                    decoder.save_pretrained(SCREAMING_SNAKE_CASE__  )
                    a_      :	int							=						MaMaaaDecoder.from_pretrained(SCREAMING_SNAKE_CASE__  ).to(SCREAMING_SNAKE_CASE__  )

             a_      :	Union[str, Any]							=						decoder(
                 input_ids=inputs_dict['decoder_input_ids']  ,							attention_mask=inputs_dict['decoder_attention_mask']  ,							encoder_hidden_states=SCREAMING_SNAKE_CASE__  ,							encoder_attention_mask=inputs_dict['attention_mask']  ,							)[0]

             self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3  )
@require_torch
class 				SCREAMING_SNAKE_CASE__						(       lowercase__		,					lowercase__		,					lowercase__		,					unittest.TestCase    ):
      snake_case__       :  Optional[int]       =		(
          (
              MaMaaaModel,
              MaMaaaForConditionalGeneration,
          )
          if is_torch_available()
          else ()
      )
      snake_case__       :  Optional[int]       =		(MaMaaaForConditionalGeneration,) if is_torch_available() else ()
      snake_case__       :  Union[str, Any]       =		(
          {
              '''conversational''': MaMaaaForConditionalGeneration,
              '''feature-extraction''': MaMaaaModel,
              '''summarization''': MaMaaaForConditionalGeneration,
              '''text2text-generation''': MaMaaaForConditionalGeneration,
              '''translation''': MaMaaaForConditionalGeneration,
          }
          if is_torch_available()
          else {}
      )
      snake_case__       :  str       =		True
      snake_case__       :  Dict       =		True
      snake_case__       :  Dict       =		False
      snake_case__       :  Any       =		False
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : int  ,							SCREAMING_SNAKE_CASE__       : Any  ,							SCREAMING_SNAKE_CASE__       : str  ,							SCREAMING_SNAKE_CASE__       : Optional[int]  ,							SCREAMING_SNAKE_CASE__       : List[Any]  )	->    int:
             if pipeline_test_casse_name == "TranslationPipelineTests":
                    # Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
                    # `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
                    return True

             return False
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[str]:
             a_      :	Optional[int]							=						MaMaaaModelTester(self  )
             a_      :	Optional[int]							=						ConfigTester(self  ,							config_class=SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    str:
             self.config_tester.run_common_tests()
      def  SCREAMING_SNAKE_CASE							( self       : Optional[Any]  )	->    Optional[Any]:
             a_							,	a_      :	Optional[int]							=						self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                    a_      :	List[Any]							=						model_class(SCREAMING_SNAKE_CASE__  )

                    with tempfile.TemporaryDirectory() as tmpdirname:
                           model.save_pretrained(SCREAMING_SNAKE_CASE__  )
                           a_							,	a_      :	Optional[Any]							=						model_class.from_pretrained(SCREAMING_SNAKE_CASE__  ,							output_loading_info=SCREAMING_SNAKE_CASE__  )
                    self.assertEqual(info['missing_keys']  ,							[]  )
      def  SCREAMING_SNAKE_CASE							( self       : str  )	->    Tuple:
             a_      :	Dict							=						self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Any:
             a_      :	Optional[Any]							=						self.model_tester.prepare_config_and_inputs_for_common()
             self.model_tester.check_encoder_decoder_model_standalone(*SCREAMING_SNAKE_CASE__  )
      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    List[Any]:
             a_							,	a_      :	int							=						self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
                    a_      :	str							=						model_class(SCREAMING_SNAKE_CASE__  )
                    model.to(SCREAMING_SNAKE_CASE__  )
                    model.eval()

                    a_      :	List[str]							=						copy.deepcopy(self._prepare_for_class(SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )  )

                    if not self.is_encoder_decoder:
                           a_      :	int							=						inputs['input_ids']
                           del inputs["input_ids"]
                    else:
                           a_      :	str							=						inputs['input_ids']
                           a_      :	str							=						inputs.get('decoder_input_ids'  ,							SCREAMING_SNAKE_CASE__  )
                           del inputs["input_ids"]
                           inputs.pop('decoder_input_ids'  ,							SCREAMING_SNAKE_CASE__  )

                    a_      :	List[str]							=						model.get_input_embeddings()
                    if not self.is_encoder_decoder:
                           a_      :	Any							=						wte(SCREAMING_SNAKE_CASE__  )
                    else:
                           a_      :	Optional[int]							=						wte(SCREAMING_SNAKE_CASE__  )
                           a_      :	Any							=						wte(SCREAMING_SNAKE_CASE__  )

                    with torch.no_grad():
                           model(**SCREAMING_SNAKE_CASE__  )[0]

      def  SCREAMING_SNAKE_CASE							( self       : Tuple  )	->    Dict:
             a_							,	a_      :	Dict							=						self.model_tester.prepare_config_and_inputs()
             a_      :	Union[str, Any]							=						input_dict['input_ids']
             a_      :	List[Any]							=						input_ids.ne(1  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	List[str]							=						MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE__  ).eval().to(SCREAMING_SNAKE_CASE__  )
             if torch_device == "cuda":
                    model.half()
             model.generate(SCREAMING_SNAKE_CASE__  ,							attention_mask=SCREAMING_SNAKE_CASE__  )
             model.generate(num_beams=4  ,							do_sample=SCREAMING_SNAKE_CASE__  ,							early_stopping=SCREAMING_SNAKE_CASE__  ,							num_return_sequences=3  )







def  SCREAMING_SNAKE_CASE_		(    __A				:    Any       )			->	Optional[int]:

       """simple docstring"""

       return torch.tensor(__A						,					dtype=torch.long						,					device=__A       )


UpperCAmelCase_   :				int						      = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class 				SCREAMING_SNAKE_CASE__						(       unittest.TestCase    ):
      @cached_property
      def  SCREAMING_SNAKE_CASE							( self       : List[str]  )	->    Tuple:
             return MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'  )
      def  SCREAMING_SNAKE_CASE							( self       : List[Any]  )	->    Union[str, Any]:
             a_      :	Union[str, Any]							=						MaMaaaModel.from_pretrained('facebook/m2m100_418M'  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	List[Any]							=						_long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]  )
             a_      :	List[Any]							=						_long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]  )
             a_      :	List[Any]							=						prepare_mam_aaa_inputs_dict(model.config  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             with torch.no_grad():
                    a_      :	Tuple							=						model(**SCREAMING_SNAKE_CASE__  )[0]
             a_      :	int							=						torch.Size((1, 1_1, 1_0_2_4)  )
             self.assertEqual(output.shape  ,							SCREAMING_SNAKE_CASE__  )
             # change to expected output here
             a_      :	Optional[Any]							=						torch.tensor(
                 [[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]]  ,							device=SCREAMING_SNAKE_CASE__  )
             self.assertTrue(torch.allclose(output[:, :3, :3]  ,							SCREAMING_SNAKE_CASE__  ,							atol=SCREAMING_SNAKE_CASE__  )  )
      def  SCREAMING_SNAKE_CASE							( self       : Optional[int]  )	->    List[Any]:
             a_      :	int							=						MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'  ).to(SCREAMING_SNAKE_CASE__  )

             # change to intended input
             a_      :	List[str]							=						_long_tensor([[1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8, 2]]  )
             a_      :	Optional[int]							=						_long_tensor([[2, 1_2_8_0_2_8, 9_8, 1_2, 3_0_5_2_7, 2_7_3_2, 1_5_9, 7_7_5_5, 6_1_9_0_4, 3_9_1_4_4, 3_8]]  )
             a_      :	int							=						prepare_mam_aaa_inputs_dict(model.config  ,							SCREAMING_SNAKE_CASE__  ,							SCREAMING_SNAKE_CASE__  )
             with torch.no_grad():
                    a_      :	List[str]							=						model(**SCREAMING_SNAKE_CASE__  )[0]
             a_      :	int							=						torch.Size((1, 1_1, model.config.vocab_size)  )
             self.assertEqual(output.shape  ,							SCREAMING_SNAKE_CASE__  )
             # change to expected output here
             a_      :	Dict							=						torch.tensor(
                 [[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]]  ,							device=SCREAMING_SNAKE_CASE__  )
             self.assertTrue(torch.allclose(output[:, :3, :3]  ,							SCREAMING_SNAKE_CASE__  ,							atol=SCREAMING_SNAKE_CASE__  )  )

      def  SCREAMING_SNAKE_CASE							( self       : int  )	->    Tuple:
             a_      :	Dict							=						MaMaaaForConditionalGeneration.from_pretrained('facebook/m2m100_418M'  ).to(SCREAMING_SNAKE_CASE__  )
             a_      :	str							=						MaMaaaTokenizer.from_pretrained('facebook/m2m100_418M'  ,							src_lang='fr'  ,							tgt_lang='en'  )

             a_      :	List[str]							=						[
                 'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
                 'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
                 'Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent'
                 ' Fabius convoque l\'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de'
                 ' l\'ampleur de la surveillance américaine sur l\'ensemble des communications en France.',
             ]

             # The below article tests that we don't add any hypotheses outside of the top n_beams
             a_      :	Any							=						tokenizer(SCREAMING_SNAKE_CASE__  ,							padding=SCREAMING_SNAKE_CASE__  ,							return_tensors='pt'  )

             a_      :	Optional[int]							=						model.generate(
                 input_ids=dct['input_ids'].to(SCREAMING_SNAKE_CASE__  )  ,							attention_mask=dct['attention_mask'].to(SCREAMING_SNAKE_CASE__  )  ,							num_beams=5  ,							forced_bos_token_id=tokenizer.get_lang_id('en'  )  ,							)

             a_      :	Any							=						[
                 'The NSA case highlights the total absence of intelligence debate',
                 'I think there are two levels of response from the French government.',
                 'When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S.'
                 ' Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all'
                 ' communications in France.',
             ]

             a_      :	int							=						tokenizer.batch_decode(
                 hypotheses_batch.tolist()  ,							clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__  ,							skip_special_tokens=SCREAMING_SNAKE_CASE__  )
             assert generated == expected_en


