


"""simple docstring"""


from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


__lowerCamelCase   :Dict						     =		logging.get_logger(__name__)


__lowerCamelCase   :Optional[int]						     =		OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

__lowerCamelCase   :Tuple						     =		OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

__lowerCamelCase   :List[str]						     =		OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

__lowerCamelCase   :Any						     =		OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

__lowerCamelCase   :Any						     =		OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

__lowerCamelCase   :Dict						     =		OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

__lowerCamelCase   :Optional[Any]						     =		OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

__lowerCamelCase   :Dict						     =		OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

__lowerCamelCase   :Tuple						     =		OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

__lowerCamelCase   :Any						     =		OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

__lowerCamelCase   :int						     =		OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

__lowerCamelCase   :Optional[int]						     =		OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

__lowerCamelCase   :int						     =		OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

__lowerCamelCase   :Any						     =		OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

__lowerCamelCase   :str						     =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__lowerCamelCase   :str						     =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__lowerCamelCase   :Dict						     =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__lowerCamelCase   :List[Any]						     =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__lowerCamelCase   :List[str]						     =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__lowerCamelCase   :Tuple						     =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__lowerCamelCase   :Any						     =		_LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__lowerCamelCase   :Any						     =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__lowerCamelCase   :Optional[Any]						     =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__lowerCamelCase   :Tuple						     =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__lowerCamelCase   :Union[str, Any]						     =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__lowerCamelCase   :Optional[int]						     =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__lowerCamelCase   :Tuple						     =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__lowerCamelCase   :Optional[int]						     =		_LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       List[str]				    =FLAX_MODEL_MAPPING


__lowerCamelCase   :str						     =		auto_class_update(FlaxAutoModel)


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       str				    =FLAX_MODEL_FOR_PRETRAINING_MAPPING


__lowerCamelCase   :Tuple						     =		auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       Dict				    =FLAX_MODEL_FOR_CAUSAL_LM_MAPPING


__lowerCamelCase   :Dict						     =		auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       List[str]				    =FLAX_MODEL_FOR_MASKED_LM_MAPPING


__lowerCamelCase   :str						     =		auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       int				    =FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING


__lowerCamelCase   :int						     =		auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       Union[str, Any]				    =FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


__lowerCamelCase   :List[str]						     =		auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       Any				    =FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING


__lowerCamelCase   :Dict						     =		auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       Union[str, Any]				    =FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING


__lowerCamelCase   :Union[str, Any]						     =		auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       List[Any]				    =FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING


__lowerCamelCase   :Tuple						     =		auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       Tuple				    =FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING


__lowerCamelCase   :Optional[Any]						     =		auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       List[Any]				    =FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


__lowerCamelCase   :Optional[Any]						     =		auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       int				    =FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING


__lowerCamelCase   :List[Any]						     =		auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')


class       A__						(    _BaseAutoModelClass):


     """simple docstring"""
     snake_case__						:       int				    =FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING


__lowerCamelCase   :Union[str, Any]						     =		auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)










'''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


lowerCAmelCase_      :  Optional[Any]				     =  get_tests_dir('fixtures/test_sentencepiece.model')

lowerCAmelCase_      :  int				     =  {'target_lang': 'fi', 'source_lang': 'en'}
lowerCAmelCase_      :  str				     =  '>>zh<<'
lowerCAmelCase_      :  List[str]				     =  'Helsinki-NLP/'

if is_torch_available():
 lowerCAmelCase_      :  Dict				     =  'pt'
elif is_tf_available():
 lowerCAmelCase_      :  Union[str, Any]				     =  'tf'
else:
 lowerCAmelCase_      :  int				     =  'jax'







@require_sentencepiece
class      SCREAMING_SNAKE_CASE    (						snake_case_							,      unittest.TestCase				):
      __magic_name__  :       Dict   							=					MarianTokenizer
      __magic_name__  :       Any   							=					False
      __magic_name__  :       str   							=					True






      def 							lowercase_						( self   :  Any       ):


           '''simple docstring'''

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

           a_     :							Dict     						=  MarianTokenizer.from_pretrained(self.tmpdirname       )
           tokenizer.save_pretrained(self.tmpdirname       )






      def 							lowercase_						( self   :  Tuple						,						**lowercase__   :  int       ):


           '''simple docstring'''

           return MarianTokenizer.from_pretrained(self.tmpdirname						,						**lowercase__       )






      def 							lowercase_						( self   :  int						,						lowercase__   :  int       ):


           '''simple docstring'''

           return (
               "This is a test",
               "This is a test",
           )






      def 							lowercase_						( self   :  List[str]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  """</s>"""
           a_     :							Optional[int]     						=  0

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase__       )						,						lowercase__       )
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase__       )						,						lowercase__       )






      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  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(lowercase__       )						,						9       )






      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size						,						9       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							str     						=  MarianTokenizer.from_pretrained(F"{ORG_NAME}opus-mt-en-de"       )
           a_     :							Any     						=  en_de_tokenizer(["""I am a small frog"""]						,						return_tensors=lowercase__       )
           self.assertIsInstance(lowercase__						,						lowercase__       )
           a_     :							str     						=  [38, 121, 14, 697, 3_8848, 0]
           self.assertListEqual(lowercase__						,						batch.input_ids[0]       )

           a_     :							Union[str, Any]     						=  tempfile.mkdtemp()
           en_de_tokenizer.save_pretrained(lowercase__       )
           a_     :							Union[str, Any]     						=  [x.name for x in Path(lowercase__       ).glob("""*"""       )]
           self.assertIn("""source.spm"""						,						lowercase__       )
           MarianTokenizer.from_pretrained(lowercase__       )






      def 							lowercase_						( self   :  str       ):


           '''simple docstring'''

           a_     :							int     						=  self.get_tokenizer()

           a_     :							Dict     						=  tok(
               ["""I am a small frog""" * 1000, """I am a small frog"""]						,						padding=lowercase__						,						truncation=lowercase__						,						return_tensors=lowercase__       )
           self.assertIsInstance(lowercase__						,						lowercase__       )
           self.assertEqual(batch.input_ids.shape						,						(2, 512)       )






      def 							lowercase_						( self   :  Optional[Any]       ):


           '''simple docstring'''

           a_     :							List[str]     						=  self.get_tokenizer()
           a_     :							Dict     						=  tok(["""I am a tiny frog""", """I am a small frog"""]						,						padding=lowercase__						,						return_tensors=lowercase__       )
           self.assertIsInstance(lowercase__						,						lowercase__       )
           self.assertEqual(batch_smaller.input_ids.shape						,						(2, 10)       )






      @slow
      def 							lowercase_						( self   :  List[Any]       ):


           '''simple docstring'''

           a_     :							Optional[int]     						=  {"""input_ids""": [[4_3495, 462, 20, 4_2164, 1369, 52, 464, 132, 1703, 492, 13, 7491, 3_8999, 6, 8, 464, 132, 1703, 492, 13, 4669, 3_7867, 13, 7525, 27, 1593, 988, 13, 3_3972, 7029, 6, 20, 8251, 383, 2, 270, 5866, 3788, 2, 2353, 8251, 1_2338, 2, 1_3958, 387, 2, 3629, 6953, 188, 2900, 2, 1_3958, 8011, 1_1501, 23, 8460, 4073, 3_4009, 20, 435, 1_1439, 27, 8, 8460, 4073, 6004, 20, 9988, 375, 27, 33, 266, 1945, 1076, 1350, 3_7867, 3288, 5, 577, 1076, 4374, 8, 5082, 5, 2_6453, 257, 556, 403, 2, 242, 132, 383, 316, 492, 8, 1_0767, 6, 316, 304, 4239, 3, 0], [148, 1_5722, 19, 1839, 12, 1350, 13, 2_2327, 5082, 5418, 4_7567, 3_5938, 59, 318, 1_9552, 108, 2183, 54, 1_4976, 4835, 32, 547, 1114, 8, 315, 2417, 5, 92, 1_9088, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100], [36, 6395, 1_2570, 3_9147, 1_1597, 6, 266, 4, 4_5405, 7296, 3, 0, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100, 5_8100]], """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=lowercase__						,						model_name="""Helsinki-NLP/opus-mt-en-de"""						,						revision="""1a8c2263da11e68e50938f97e10cd57820bd504c"""						,						decode_kwargs={"""use_source_tokenizer""": True}						,						)






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

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

           a_     :							Tuple     						=  """Tämä on testi"""
           a_     :							Union[str, Any]     						=  """This is a test"""

           a_     :							Union[str, Any]     						=  [76, 7, 2047, 2]
           a_     :							Optional[int]     						=  [69, 12, 11, 940, 2]

           a_     :							Optional[Any]     						=  tokenizer(lowercase__       ).input_ids
           self.assertListEqual(lowercase__						,						lowercase__       )

           a_     :							Optional[int]     						=  tokenizer(text_target=lowercase__       ).input_ids
           self.assertListEqual(lowercase__						,						lowercase__       )

           a_     :							str     						=  tokenizer.decode(lowercase__						,						skip_special_tokens=lowercase__       )
           self.assertEqual(lowercase__						,						lowercase__       )
