


"""simple docstring"""

from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
       import tensorflow as tf

       from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel





@require_tf
class  lowerCamelCase__    :
      a			:  str           =		BlenderbotSmallConfig
      a			:  List[Any]           =		{}
      a			:  int           =		"""gelu"""
      def __init__(		self						:						str   ,							A_						:						int   ,							A_						:						Dict=1_3   ,							A_						:						Optional[Any]=7   ,							A_						:						Dict=True   ,							A_						:						List[str]=False   ,							A_						:						str=9_9   ,							A_						:						List[Any]=3_2   ,							A_						:						Tuple=2   ,							A_						:						Dict=4   ,							A_						:						int=3_7   ,							A_						:						List[str]=0.1   ,							A_						:						Tuple=0.1   ,							A_						:						int=2_0   ,							A_						:						Optional[Any]=2   ,							A_						:						List[str]=1   ,							A_						:						List[Any]=0   ,							):






       '''simple docstring'''


       __lowercase						=						parent
       __lowercase						=						batch_size
       __lowercase						=						seq_length
       __lowercase						=						is_training
       __lowercase						=						use_labels
       __lowercase						=						vocab_size
       __lowercase						=						hidden_size
       __lowercase						=						num_hidden_layers
       __lowercase						=						num_attention_heads
       __lowercase						=						intermediate_size

       __lowercase						=						hidden_dropout_prob
       __lowercase						=						attention_probs_dropout_prob
       __lowercase						=						max_position_embeddings
       __lowercase						=						eos_token_id
       __lowercase						=						pad_token_id
       __lowercase						=						bos_token_id
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						ids_tensor([self.batch_size, self.seq_length - 1]   ,							self.vocab_size				)
       __lowercase						=						tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size				)   ,							1				)
       __lowercase						=						tf.concat([input_ids, eos_tensor]   ,							axis=1				)

       __lowercase						=						ids_tensor([self.batch_size, self.seq_length]   ,							self.vocab_size				)

       __lowercase						=						self.config_cls(
           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   ,							max_position_embeddings=self.max_position_embeddings   ,							eos_token_ids=[2]   ,							bos_token_id=self.bos_token_id   ,							pad_token_id=self.pad_token_id   ,							decoder_start_token_id=self.pad_token_id   ,							**self.config_updates   ,							)
       __lowercase						=						prepare_blenderbot_small_inputs_dict(A_   ,							A_   ,							A_				)
       return config, inputs_dict






      def SCREAMING_SNAKE_CASE_						(		self						:						Any   ,							A_						:						str   ,							A_						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						TFBlenderbotSmallModel(config=A_				).get_decoder()
       __lowercase						=						inputs_dict["""input_ids"""]

       __lowercase						=						input_ids[:1, :]
       __lowercase						=						inputs_dict["""attention_mask"""][:1, :]
       __lowercase						=						inputs_dict["""head_mask"""]
       __lowercase						=						1

       # first forward pass
       __lowercase						=						model(A_   ,							attention_mask=A_   ,							head_mask=A_   ,							use_cache=A_				)

       __lowercase				,		__lowercase						=						outputs.to_tuple()

       # create hypothetical next token and extent to next_input_ids
       __lowercase						=						ids_tensor((self.batch_size, 3)   ,							config.vocab_size				)
       __lowercase						=						tf.cast(ids_tensor((self.batch_size, 3)   ,							2				)   ,							tf.inta				)

       # append to next input_ids and
       __lowercase						=						tf.concat([input_ids, next_tokens]   ,							axis=-1				)
       __lowercase						=						tf.concat([attention_mask, next_attn_mask]   ,							axis=-1				)

       __lowercase						=						model(A_   ,							attention_mask=A_				)[0]
       __lowercase						=						model(A_   ,							attention_mask=A_   ,							past_key_values=A_				)[0]

       self.parent.assertEqual(next_tokens.shape[1]   ,							output_from_past.shape[1]				)

       # select random slice
       __lowercase						=						int(ids_tensor((1,)   ,							output_from_past.shape[-1]				)				)
       __lowercase						=						output_from_no_past[:, -3:, random_slice_idx]
       __lowercase						=						output_from_past[:, :, random_slice_idx]

       # test that outputs are equal for slice
       tf.debugging.assert_near(A_   ,							A_   ,							rtol=1e-3				)






def lowerCAmelCase_   (					UpperCamelCase__						:			List[Any]					, UpperCamelCase__						:			str					, UpperCamelCase__						:			Dict					, UpperCamelCase__						:			Any=None					, UpperCamelCase__						:			Any=None					, UpperCamelCase__						:			str=None					, UpperCamelCase__						:			str=None					, UpperCamelCase__						:			Any=None					, ):






 """simple docstring"""







 if attention_mask is None:
  __lowercase						=						tf.cast(tf.math.not_equal(UpperCamelCase__					, config.pad_token_id			)					, tf.inta			)
 if decoder_attention_mask is None:
  __lowercase						=						tf.concat(
      [
          tf.ones(decoder_input_ids[:, :1].shape					, dtype=tf.inta			),
          tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]					, config.pad_token_id			)					, tf.inta			),
      ]					, axis=-1					, )
 if head_mask is None:
  __lowercase						=						tf.ones((config.encoder_layers, config.encoder_attention_heads)			)
 if decoder_head_mask is None:
  __lowercase						=						tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
 if cross_attn_head_mask is None:
  __lowercase						=						tf.ones((config.decoder_layers, config.decoder_attention_heads)			)
 return {
     "input_ids": input_ids,
     "decoder_input_ids": decoder_input_ids,
     "attention_mask": attention_mask,
     "decoder_attention_mask": decoder_attention_mask,
     "head_mask": head_mask,
     "decoder_head_mask": decoder_head_mask,
     "cross_attn_head_mask": cross_attn_head_mask,
 }





@require_tf
class  lowerCamelCase__    (		_a      , _a      , unittest.TestCase     ):
      a			:  Union[str, Any]           =		(
          (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
      )
      a			:  List[str]           =		(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
      a			:  Dict           =		(
          {
              """conversational""": TFBlenderbotSmallForConditionalGeneration,
              """feature-extraction""": TFBlenderbotSmallModel,
              """summarization""": TFBlenderbotSmallForConditionalGeneration,
              """text2text-generation""": TFBlenderbotSmallForConditionalGeneration,
              """translation""": TFBlenderbotSmallForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      a			:  Any           =		True
      a			:  List[Any]           =		False
      a			:  Optional[int]           =		False
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						TFBlenderbotSmallModelTester(self				)
       __lowercase						=						ConfigTester(self   ,							config_class=A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       self.config_tester.run_common_tests()






      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.model_tester.prepare_config_and_inputs_for_common()
       self.model_tester.check_decoder_model_past_large_inputs(*A_				)






@require_tokenizers
@require_tf
class  lowerCamelCase__    (		unittest.TestCase     ):
      a			:  int           =		[
          """Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like """
          """  i'm going to throw up.\nand why is that?"""
      ]
      a			:  List[str]           =		"""facebook/blenderbot_small-90M"""
      @cached_property
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       return BlenderbotSmallTokenizer.from_pretrained("""facebook/blenderbot-90M"""				)
      @cached_property
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name				)
       return model






      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						self.tokenizer(self.src_text   ,							return_tensors="""tf"""				)
       __lowercase						=						self.model.generate(
           model_inputs.input_ids   ,							attention_mask=model_inputs.attention_mask   ,							num_beams=2   ,							use_cache=A_   ,							)
       __lowercase						=						self.tokenizer.batch_decode(generated_ids.numpy()   ,							skip_special_tokens=A_				)[0]
       assert generated_words in (
           "i don't know. i just feel like i'm going to throw up. it's not fun.",
           "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
           "i'm not sure. i just feel like i've been in a bad situation.",
       )




"""simple docstring"""

import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available, is_torch_available


if is_sentencepiece_available():
       from transformers.models.marian.tokenization_marian import VOCAB_FILES_NAMES, save_json

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__					=get_tests_dir("fixtures/test_sentencepiece.model")

UpperCAmelCase__					={"target_lang": "fi", "source_lang": "en"}
UpperCAmelCase__					=">>zh<<"
UpperCAmelCase__					="Helsinki-NLP/"

if is_torch_available():
       UpperCAmelCase__					="pt"
elif is_tf_available():
       UpperCAmelCase__					="tf"
else:
       UpperCAmelCase__					="jax"





@require_sentencepiece
class  lowerCamelCase__    (		_a      , unittest.TestCase     ):
      a			:  Dict           =		MarianTokenizer
      a			:  Optional[int]           =		False
      a			:  Any           =		True
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       super().setUp()
       __lowercase						=						["""</s>""", """<unk>""", """▁This""", """▁is""", """▁a""", """▁t""", """est""", """\u0120""", """<pad>"""]
       __lowercase						=						dict(zip(A_   ,							range(len(A_				)				)				)				)
       __lowercase						=						Path(self.tmpdirname				)
       save_json(A_   ,							save_dir / VOCAB_FILES_NAMES["""vocab"""]				)
       save_json(A_   ,							save_dir / VOCAB_FILES_NAMES["""tokenizer_config_file"""]				)
       if not (save_dir / VOCAB_FILES_NAMES["source_spm"]).exists():
        copyfile(A_   ,							save_dir / VOCAB_FILES_NAMES["""source_spm"""]				)
        copyfile(A_   ,							save_dir / VOCAB_FILES_NAMES["""target_spm"""]				)

       __lowercase						=						MarianTokenizer.from_pretrained(self.tmpdirname				)
       tokenizer.save_pretrained(self.tmpdirname				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict   ,							**A_						:						Any				):






       '''simple docstring'''


       return MarianTokenizer.from_pretrained(self.tmpdirname   ,							**A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						int   ,							A_						:						List[str]				):






       '''simple docstring'''


       return (
           "This is a test",
           "This is a test",
       )
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       __lowercase						=						"""</s>"""
       __lowercase						=						0

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_				)   ,							A_				)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						list(self.get_tokenizer().get_vocab().keys()				)

       self.assertEqual(vocab_keys[0]   ,							"""</s>"""				)
       self.assertEqual(vocab_keys[1]   ,							"""<unk>"""				)
       self.assertEqual(vocab_keys[-1]   ,							"""<pad>"""				)
       self.assertEqual(len(A_				)   ,							9				)
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       self.assertEqual(self.get_tokenizer().vocab_size   ,							9				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						MarianTokenizer.from_pretrained(F'''{ORG_NAME}opus-mt-en-de'''				)
       __lowercase						=						en_de_tokenizer(["""I am a small frog"""]   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       __lowercase						=						[3_8, 1_2_1, 1_4, 6_9_7, 3_8_8_4_8, 0]
       self.assertListEqual(A_   ,							batch.input_ids[0]				)

       __lowercase						=						tempfile.mkdtemp()
       en_de_tokenizer.save_pretrained(A_				)
       __lowercase						=						[x.name for x in Path(A_				).glob("""*"""				)]
       self.assertIn("""source.spm"""   ,							A_				)
       MarianTokenizer.from_pretrained(A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.get_tokenizer()

       __lowercase						=						tok(
           ["""I am a small frog""" * 1_0_0_0, """I am a small frog"""]   ,							padding=A_   ,							truncation=A_   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       self.assertEqual(batch.input_ids.shape   ,							(2, 5_1_2)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						self.get_tokenizer()
       __lowercase						=						tok(["""I am a tiny frog""", """I am a small frog"""]   ,							padding=A_   ,							return_tensors=A_				)
       self.assertIsInstance(A_   ,							A_				)
       self.assertEqual(batch_smaller.input_ids.shape   ,							(2, 1_0)				)
      @slow
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						{"""input_ids""": [[4_3_4_9_5, 4_6_2, 2_0, 4_2_1_6_4, 1_3_6_9, 5_2, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 7_4_9_1, 3_8_9_9_9, 6, 8, 4_6_4, 1_3_2, 1_7_0_3, 4_9_2, 1_3, 4_6_6_9, 3_7_8_6_7, 1_3, 7_5_2_5, 2_7, 1_5_9_3, 9_8_8, 1_3, 3_3_9_7_2, 7_0_2_9, 6, 2_0, 8_2_5_1, 3_8_3, 2, 2_7_0, 5_8_6_6, 3_7_8_8, 2, 2_3_5_3, 8_2_5_1, 1_2_3_3_8, 2, 1_3_9_5_8, 3_8_7, 2, 3_6_2_9, 6_9_5_3, 1_8_8, 2_9_0_0, 2, 1_3_9_5_8, 8_0_1_1, 1_1_5_0_1, 2_3, 8_4_6_0, 4_0_7_3, 3_4_0_0_9, 2_0, 4_3_5, 1_1_4_3_9, 2_7, 8, 8_4_6_0, 4_0_7_3, 6_0_0_4, 2_0, 9_9_8_8, 3_7_5, 2_7, 3_3, 2_6_6, 1_9_4_5, 1_0_7_6, 1_3_5_0, 3_7_8_6_7, 3_2_8_8, 5, 5_7_7, 1_0_7_6, 4_3_7_4, 8, 5_0_8_2, 5, 2_6_4_5_3, 2_5_7, 5_5_6, 4_0_3, 2, 2_4_2, 1_3_2, 3_8_3, 3_1_6, 4_9_2, 8, 1_0_7_6_7, 6, 3_1_6, 3_0_4, 4_2_3_9, 3, 0], [1_4_8, 1_5_7_2_2, 1_9, 1_8_3_9, 1_2, 1_3_5_0, 1_3, 2_2_3_2_7, 5_0_8_2, 5_4_1_8, 4_7_5_6_7, 3_5_9_3_8, 5_9, 3_1_8, 1_9_5_5_2, 1_0_8, 2_1_8_3, 5_4, 1_4_9_7_6, 4_8_3_5, 3_2, 5_4_7, 1_1_1_4, 8, 3_1_5, 2_4_1_7, 5, 9_2, 1_9_0_8_8, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0], [3_6, 6_3_9_5, 1_2_5_7_0, 3_9_1_4_7, 1_1_5_9_7, 6, 2_6_6, 4, 4_5_4_0_5, 7_2_9_6, 3, 0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0, 5_8_1_0_0]], """attention_mask""": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
       # fmt: on

       self.tokenizer_integration_test_util(
           expected_encoding=A_   ,							model_name="""Helsinki-NLP/opus-mt-en-de"""   ,							revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""   ,							decode_kwargs={"""use_source_tokenizer""": True}   ,							)






      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						MarianTokenizer.from_pretrained("""hf-internal-testing/test-marian-two-vocabs"""				)

       __lowercase						=						"""Tämä on testi"""
       __lowercase						=						"""This is a test"""

       __lowercase						=						[7_6, 7, 2_0_4_7, 2]
       __lowercase						=						[6_9, 1_2, 1_1, 9_4_0, 2]

       __lowercase						=						tokenizer(A_				).input_ids
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer(text_target=A_				).input_ids
       self.assertListEqual(A_   ,							A_				)

       __lowercase						=						tokenizer.decode(A_   ,							skip_special_tokens=A_				)
       self.assertEqual(A_   ,							A_				)
