




'''simple docstring'''


import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					int ,    UpperCamelCase__    :					str				):




     """simple docstring"""


     return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					str ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					Dict="attention"				):




     """simple docstring"""


     a_     :							int     						=  np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]				)
     a_     :							List[Any]     						=  k_tmp.reshape(k_tmp.shape[0] ,    k_tmp.shape[1] * k_tmp.shape[2]				)
     a_     :							List[str]     						=  np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]				)
     a_     :							List[Any]     						=  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,    o_tmp.shape[2]				)
     a_     :							List[str]     						=  np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]				)
     a_     :							str     						=  q_tmp.reshape(q_tmp.shape[0] ,    q_tmp.shape[1] * q_tmp.shape[2]				)
     a_     :							List[Any]     						=  np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]				)
     a_     :							Optional[int]     						=  v_tmp.reshape(v_tmp.shape[0] ,    v_tmp.shape[1] * v_tmp.shape[2]				)
     return k, o, q, v



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					Any ,    UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Dict=False				):




     """simple docstring"""


     if split_mlp_wi:
          a_     :							List[Any]     						=  params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
          a_     :							Any     						=  params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
          a_     :							Dict     						=  (wi_a, wi_a)
     else:
          a_     :							Dict     						=  params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

     a_     :							List[str]     						=  params[F"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
     return wi, wo



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Any ,    UpperCamelCase__    :					int ,    UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					Tuple				):




     """simple docstring"""


     return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					dict ,    *, UpperCamelCase__    :					int ,    UpperCamelCase__    :					bool ,    UpperCamelCase__    :					bool = False				):




     """simple docstring"""


     a_     :							int     						=  traverse_util.flatten_dict(variables["""target"""]				)
     a_     :							Dict     						=  {"""/""".join(UpperCamelCase__				): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     a_     :							Any     						=  """encoder/encoder/mlp/wi_0/kernel""" in old
     print("""Split MLP:""" ,    UpperCamelCase__				)

     a_     :							Any     						=  collections.OrderedDict()

     # Shared embeddings.
     a_     :							Optional[int]     						=  old["""token_embedder/embedding"""]

     # Encoder.
     for i in range(UpperCamelCase__				):
          # Block i, layer 0 (Self Attention).
          a_     :							Tuple     						=  tax_layer_norm_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """encoder""" ,    """pre_attention_layer_norm"""				)
          a_						,     a_						,     a_						,     a_     :							List[str]     						=  tax_attention_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """encoder""" ,    """attention"""				)
          a_     :							Any     						=  layer_norm
          a_     :							Optional[Any]     						=  k.T
          a_     :							Any     						=  o.T
          a_     :							Tuple     						=  q.T
          a_     :							List[str]     						=  v.T

          # Block i, layer 1 (MLP).
          a_     :							Any     						=  tax_layer_norm_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """encoder""" ,    """pre_mlp_layer_norm"""				)
          a_						,     a_     :							List[str]     						=  tax_mlp_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """encoder""" ,    UpperCamelCase__				)
          a_     :							str     						=  layer_norm
          if split_mlp_wi:
               a_     :							List[str]     						=  wi[0].T
               a_     :							int     						=  wi[1].T
          else:
               a_     :							List[str]     						=  wi.T
          a_     :							Optional[int]     						=  wo.T
          if scalable_attention:
               # convert the rel_embedding of each layer
               a_     :							Optional[int]     						=  tax_relpos_bias_lookup(
                   UpperCamelCase__ ,    UpperCamelCase__ ,    """encoder"""				).T

     a_     :							Dict     						=  old["""encoder/encoder_norm/scale"""]

     if not scalable_attention:
          a_     :							int     						=  tax_relpos_bias_lookup(
              UpperCamelCase__ ,    0 ,    """encoder"""				).T
          a_     :							List[str]     						=  tax_relpos_bias_lookup(
              UpperCamelCase__ ,    0 ,    """decoder"""				).T

     if not is_encoder_only:
          # Decoder.
          for i in range(UpperCamelCase__				):
               # Block i, layer 0 (Self Attention).
               a_     :							Optional[int]     						=  tax_layer_norm_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """decoder""" ,    """pre_self_attention_layer_norm"""				)
               a_						,     a_						,     a_						,     a_     :							str     						=  tax_attention_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """decoder""" ,    """self_attention"""				)
               a_     :							Tuple     						=  layer_norm
               a_     :							str     						=  k.T
               a_     :							Any     						=  o.T
               a_     :							Union[str, Any]     						=  q.T
               a_     :							Dict     						=  v.T

               # Block i, layer 1 (Cross Attention).
               a_     :							Union[str, Any]     						=  tax_layer_norm_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """decoder""" ,    """pre_cross_attention_layer_norm"""				)
               a_						,     a_						,     a_						,     a_     :							List[Any]     						=  tax_attention_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """decoder""" ,    """encoder_decoder_attention"""				)
               a_     :							Optional[Any]     						=  layer_norm
               a_     :							Union[str, Any]     						=  k.T
               a_     :							str     						=  o.T
               a_     :							List[Any]     						=  q.T
               a_     :							List[Any]     						=  v.T

               # Block i, layer 2 (MLP).
               a_     :							str     						=  tax_layer_norm_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """decoder""" ,    """pre_mlp_layer_norm"""				)
               a_						,     a_     :							Optional[Any]     						=  tax_mlp_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """decoder""" ,    UpperCamelCase__				)
               a_     :							Union[str, Any]     						=  layer_norm
               if split_mlp_wi:
                    a_     :							Dict     						=  wi[0].T
                    a_     :							Optional[Any]     						=  wi[1].T
               else:
                    a_     :							List[str]     						=  wi.T
               a_     :							Tuple     						=  wo.T

               if scalable_attention:
                    # convert the rel_embedding of each layer
                    a_     :							str     						=  tax_relpos_bias_lookup(UpperCamelCase__ ,    UpperCamelCase__ ,    """decoder"""				).T

          a_     :							int     						=  old["""decoder/decoder_norm/scale"""]

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               a_     :							Dict     						=  old["""decoder/logits_dense/kernel"""].T

     return new



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					Optional[int] ,    UpperCamelCase__    :					bool				):




     """simple docstring"""


     a_     :							Any     						=  collections.OrderedDict([(k, torch.from_numpy(v.copy()				)) for (k, v) in converted_params.items()]				)

     # Add what is missing.
     if "encoder.embed_tokens.weight" not in state_dict:
          a_     :							Any     						=  state_dict["""shared.weight"""]

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               a_     :							List[Any]     						=  state_dict["""shared.weight"""]

          if "lm_head.weight" not in state_dict:  # For old 1.0 models.
               print("""Using shared word embeddings as lm_head."""				)
               a_     :							List[Any]     						=  state_dict["""shared.weight"""]

     return state_dict



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[Any] ,    UpperCamelCase__    :					Optional[Any] ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					int ,    UpperCamelCase__    :					str				):




     """simple docstring"""


     a_     :							List[Any]     						=  checkpoints.load_tax_checkpoint(UpperCamelCase__				)
     a_     :							Union[str, Any]     						=  convert_tax_to_pytorch(
         UpperCamelCase__ ,    num_layers=config.num_layers ,    is_encoder_only=UpperCamelCase__ ,    scalable_attention=UpperCamelCase__				)
     a_     :							Dict     						=  make_state_dict(UpperCamelCase__ ,    UpperCamelCase__				)
     model.load_state_dict(UpperCamelCase__ ,    strict=UpperCamelCase__				)



def  _SCREAMING_SNAKE_CASE     (      UpperCamelCase__    :					List[str] ,    UpperCamelCase__    :					Union[str, Any] ,    UpperCamelCase__    :					str ,    UpperCamelCase__    :					bool = False ,    UpperCamelCase__    :					bool = False ,    ):




     """simple docstring"""


     a_     :							List[str]     						=  MTaConfig.from_json_file(UpperCamelCase__				)
     print(F"Building PyTorch model from configuration: {config}"				)
     # Non-v1.1 checkpoints could also use T5Model, but this works for all.
     # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
     if is_encoder_only:
          a_     :							Union[str, Any]     						=  UMTaEncoderModel(UpperCamelCase__				)
     else:
          a_     :							str     						=  UMTaForConditionalGeneration(UpperCamelCase__				)

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__ ,    UpperCamelCase__				)

     # Save pytorch-model
     print(F"Save PyTorch model to {pytorch_dump_path}"				)
     model.save_pretrained(UpperCamelCase__				)

     # Verify that we can load the checkpoint.
     model.from_pretrained(UpperCamelCase__				)
     print("""Done"""				)


if __name__ == "__main__":
 lowerCAmelCase_      :  Tuple				     =  argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
 # Required parameters
 parser.add_argument(
     '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
 )
 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
 )
 parser.add_argument(
     '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
 )
 parser.add_argument(
     '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
 )
 parser.add_argument(
     '--scalable_attention',
     action='store_true',
     help='Whether the model uses scaled attention (umt5 model)',
     default=False,
 )
 lowerCAmelCase_      :  Optional[Any]				     =  parser.parse_args()
 convert_tax_checkpoint_to_pytorch(
     args.tax_checkpoint_path,
     args.config_file,
     args.pytorch_dump_path,
     args.is_encoder_only,
     args.scalable_attention,
 )






'''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__       )
