



"""simple docstring"""





import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
  import os

  # The slow tests are often failing with OOM error on GPU
  # This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
  # but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
  lowerCamelCase       	=						"""platform"""

  import jax
  import jax.numpy as jnp

  from transformers import BlenderbotTokenizer
  from transformers.models.blenderbot.modeling_flax_blenderbot import (
      FlaxBlenderbotForConditionalGeneration,
      FlaxBlenderbotModel,
      shift_tokens_right,
  )
def        a__     (				lowerCAmelCase__     ,      lowerCAmelCase__     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      lowerCAmelCase__=None     ,      ):
      if attention_mask is None:
            UpperCAmelCase_      			=							np.where(input_ids != config.pad_token_id     ,      1     ,      0				)
      if decoder_attention_mask is None:
            UpperCAmelCase_      			=							np.where(decoder_input_ids != config.pad_token_id     ,      1     ,      0				)
      if head_mask is None:
            UpperCAmelCase_      			=							np.ones((config.encoder_layers, config.encoder_attention_heads)				)
      if decoder_head_mask is None:
            UpperCAmelCase_      			=							np.ones((config.decoder_layers, config.decoder_attention_heads)				)
      if cross_attn_head_mask is None:
            UpperCAmelCase_      			=							np.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": attention_mask,
      }
class 				lowercase__       :







       '''simple docstring'''


       def __init__(							self      :  int		,    _UpperCAmelCase      :  List[Any]		,    _UpperCAmelCase      :  List[str]=13		,    _UpperCAmelCase      :  Any=7		,    _UpperCAmelCase      :  Union[str, Any]=True		,    _UpperCAmelCase      :  Union[str, Any]=False		,    _UpperCAmelCase      :  Tuple=99		,    _UpperCAmelCase      :  Any=16		,    _UpperCAmelCase      :  List[Any]=2		,    _UpperCAmelCase      :  Tuple=4		,    _UpperCAmelCase      :  Optional[int]=4		,    _UpperCAmelCase      :  Optional[int]="gelu"		,    _UpperCAmelCase      :  List[Any]=0.1		,    _UpperCAmelCase      :  Optional[int]=0.1		,    _UpperCAmelCase      :  List[str]=32		,    _UpperCAmelCase      :  Optional[int]=2		,    _UpperCAmelCase      :  Dict=1		,    _UpperCAmelCase      :  List[str]=0		,    _UpperCAmelCase      :  Union[str, Any]=0.02		,    )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							parent
             UpperCAmelCase_      			=							batch_size
             UpperCAmelCase_      			=							seq_length
             UpperCAmelCase_      			=							is_training
             UpperCAmelCase_      			=							use_labels
             UpperCAmelCase_      			=							vocab_size
             UpperCAmelCase_      			=							hidden_size
             UpperCAmelCase_      			=							num_hidden_layers
             UpperCAmelCase_      			=							num_attention_heads
             UpperCAmelCase_      			=							intermediate_size
             UpperCAmelCase_      			=							hidden_act
             UpperCAmelCase_      			=							hidden_dropout_prob
             UpperCAmelCase_      			=							attention_probs_dropout_prob
             UpperCAmelCase_      			=							max_position_embeddings
             UpperCAmelCase_      			=							eos_token_id
             UpperCAmelCase_      			=							pad_token_id
             UpperCAmelCase_      			=							bos_token_id
             UpperCAmelCase_      			=							initializer_range



       def 					lowercase__    (							self      :  Union[str, Any]   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							np.clip(ids_tensor([self.batch_size, self.seq_length - 1]		,    self.vocab_size   )		,    3		,    self.vocab_size   )
             UpperCAmelCase_      			=							np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)		,    dtype=np.intaa   ))		,    -1   )

             UpperCAmelCase_      			=							shift_tokens_right(_UpperCAmelCase		,    1		,    2   )

             UpperCAmelCase_      			=							BlenderbotConfig(
                 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_id=self.eos_token_id		,    bos_token_id=self.bos_token_id		,    pad_token_id=self.pad_token_id		,    initializer_range=self.initializer_range		,    use_cache=_UpperCAmelCase		,    )
             UpperCAmelCase_      			=							prepare_blenderbot_inputs_dict(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )
             return config, inputs_dict



       def 					lowercase__    (							self      :  List[Any]   )			->						Dict:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.prepare_config_and_inputs()
             return config, inputs_dict



       def 					lowercase__    (							self      :  Dict		,    _UpperCAmelCase      :  str		,    _UpperCAmelCase      :  List[Any]		,    _UpperCAmelCase      :  Dict   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							20
             UpperCAmelCase_      			=							model_class_name(_UpperCAmelCase   )

             UpperCAmelCase_      			=							model.encode(inputs_dict["input_ids"]   )

             UpperCAmelCase_			,   UpperCAmelCase_      			=							(
                 inputs_dict["decoder_input_ids"],
                 inputs_dict["decoder_attention_mask"],
             )

             UpperCAmelCase_      			=							model.init_cache(decoder_input_ids.shape[0]		,    _UpperCAmelCase		,    _UpperCAmelCase   )
             UpperCAmelCase_      			=							jnp.ones((decoder_input_ids.shape[0], max_decoder_length)		,    dtype="i4"   )

             UpperCAmelCase_      			=							jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]		,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,    )
             UpperCAmelCase_      			=							model.decode(
                 decoder_input_ids[:, :-1]		,    _UpperCAmelCase		,    decoder_attention_mask=_UpperCAmelCase		,    past_key_values=_UpperCAmelCase		,    decoder_position_ids=_UpperCAmelCase		,    )

             UpperCAmelCase_      			=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,    dtype="i4"   )
             UpperCAmelCase_      			=							model.decode(
                 decoder_input_ids[:, -1:]		,    _UpperCAmelCase		,    decoder_attention_mask=_UpperCAmelCase		,    past_key_values=outputs_cache.past_key_values		,    decoder_position_ids=_UpperCAmelCase		,    )

             UpperCAmelCase_      			=							model.decode(_UpperCAmelCase		,    _UpperCAmelCase   )

             UpperCAmelCase_      			=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
             self.parent.assertTrue(diff < 1e-3		,    msg=F"""Max diff is {diff}"""   )







       def 					lowercase__    (							self      :  Tuple		,    _UpperCAmelCase      :  List[str]		,    _UpperCAmelCase      :  Tuple		,    _UpperCAmelCase      :  Dict   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							20
             UpperCAmelCase_      			=							model_class_name(_UpperCAmelCase   )

             UpperCAmelCase_      			=							model.encode(inputs_dict["input_ids"]   )

             UpperCAmelCase_			,   UpperCAmelCase_      			=							(
                 inputs_dict["decoder_input_ids"],
                 inputs_dict["decoder_attention_mask"],
             )

             UpperCAmelCase_      			=							jnp.concatenate(
                 [
                     decoder_attention_mask,
                     jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])   ),
                 ]		,    axis=-1		,    )

             UpperCAmelCase_      			=							model.init_cache(decoder_input_ids.shape[0]		,    _UpperCAmelCase		,    _UpperCAmelCase   )
             UpperCAmelCase_      			=							jnp.broadcast_to(
                 jnp.arange(decoder_input_ids.shape[-1] - 1   )[None, :]		,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)		,    )

             UpperCAmelCase_      			=							model.decode(
                 decoder_input_ids[:, :-1]		,    _UpperCAmelCase		,    decoder_attention_mask=_UpperCAmelCase		,    past_key_values=_UpperCAmelCase		,    decoder_position_ids=_UpperCAmelCase		,    )
             UpperCAmelCase_      			=							jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]		,    dtype="i4"   )
             UpperCAmelCase_      			=							model.decode(
                 decoder_input_ids[:, -1:]		,    _UpperCAmelCase		,    past_key_values=outputs_cache.past_key_values		,    decoder_attention_mask=_UpperCAmelCase		,    decoder_position_ids=_UpperCAmelCase		,    )

             UpperCAmelCase_      			=							model.decode(_UpperCAmelCase		,    _UpperCAmelCase		,    decoder_attention_mask=_UpperCAmelCase   )

             UpperCAmelCase_      			=							np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])   )   )
             self.parent.assertTrue(diff < 1e-3		,    msg=F"""Max diff is {diff}"""   )



@require_flax
class 				lowercase__       (   unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					99



       def 					lowercase__    (							self      :  Tuple   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							np.array(
                 [
                     [71, 82, 18, 33, 46, 91, 2],
                     [68, 34, 26, 58, 30, 82, 2],
                     [5, 97, 17, 39, 94, 40, 2],
                     [76, 83, 94, 25, 70, 78, 2],
                     [87, 59, 41, 35, 48, 66, 2],
                     [55, 13, 16, 58, 5, 2, 1],  # note padding
                     [64, 27, 31, 51, 12, 75, 2],
                     [52, 64, 86, 17, 83, 39, 2],
                     [48, 61, 9, 24, 71, 82, 2],
                     [26, 1, 60, 48, 22, 13, 2],
                     [21, 5, 62, 28, 14, 76, 2],
                     [45, 98, 37, 86, 59, 48, 2],
                     [70, 70, 50, 9, 28, 0, 2],
                 ]		,    dtype=np.intaa		,    )

             UpperCAmelCase_      			=							input_ids.shape[0]
             UpperCAmelCase_      			=							BlenderbotConfig(
                 vocab_size=self.vocab_size		,    d_model=24		,    encoder_layers=2		,    decoder_layers=2		,    encoder_attention_heads=2		,    decoder_attention_heads=2		,    encoder_ffn_dim=32		,    decoder_ffn_dim=32		,    max_position_embeddings=48		,    eos_token_id=2		,    pad_token_id=1		,    bos_token_id=0		,    )
             return config, input_ids, batch_size



       def 					lowercase__    (							self      :  Tuple   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_			,   UpperCAmelCase_      			=							self._get_config_and_data()
             UpperCAmelCase_      			=							FlaxBlenderbotForConditionalGeneration(_UpperCAmelCase   )
             UpperCAmelCase_      			=							lm_model(input_ids=_UpperCAmelCase   )
             UpperCAmelCase_      			=							(batch_size, input_ids.shape[1], config.vocab_size)
             self.assertEqual(outputs["logits"].shape		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  int   )			->						Optional[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							BlenderbotConfig(
                 vocab_size=self.vocab_size		,    d_model=14		,    encoder_layers=2		,    decoder_layers=2		,    encoder_attention_heads=2		,    decoder_attention_heads=2		,    encoder_ffn_dim=8		,    decoder_ffn_dim=8		,    max_position_embeddings=48		,    )
             UpperCAmelCase_      			=							FlaxBlenderbotForConditionalGeneration(_UpperCAmelCase   )
             UpperCAmelCase_      			=							np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]		,    dtype=np.intaa   )
             UpperCAmelCase_      			=							np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]		,    dtype=np.intaa   )
             UpperCAmelCase_      			=							lm_model(input_ids=_UpperCAmelCase		,    decoder_input_ids=_UpperCAmelCase   )
             UpperCAmelCase_      			=							(*summary.shape, config.vocab_size)
             self.assertEqual(outputs["logits"].shape		,    _UpperCAmelCase   )







       def 					lowercase__    (							self      :  Optional[int]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]		,    dtype=np.intaa   )
             UpperCAmelCase_      			=							shift_tokens_right(_UpperCAmelCase		,    1		,    2   )
             UpperCAmelCase_      			=							np.equal(_UpperCAmelCase		,    1   ).astype(np.floataa   ).sum()
             UpperCAmelCase_      			=							np.equal(_UpperCAmelCase		,    1   ).astype(np.floataa   ).sum()
             self.assertEqual(shifted.shape		,    input_ids.shape   )
             self.assertEqual(_UpperCAmelCase		,    n_pad_before - 1   )
             self.assertTrue(np.equal(shifted[:, 0]		,    2   ).all()   )



@require_flax
class 				lowercase__       (   SCREAMING_SNAKE_CASE    , unittest.TestCase    , SCREAMING_SNAKE_CASE				):







       '''simple docstring'''


       UpperCamelCase						=					True
       UpperCamelCase						=					(
           (
               FlaxBlenderbotModel,
               FlaxBlenderbotForConditionalGeneration,
           )
           if is_flax_available()
           else ()
       )
       UpperCamelCase						=					(FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()



       def 					lowercase__    (							self      :  int   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							FlaxBlenderbotModelTester(self   )



       def 					lowercase__    (							self      :  List[Any]   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  Tuple   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs()
             for model_class in self.all_model_classes:
                   self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCAmelCase		,    _UpperCAmelCase		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  Optional[int]   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__   ):
                         UpperCAmelCase_      			=							self._prepare_for_class(_UpperCAmelCase		,    _UpperCAmelCase   )
                         UpperCAmelCase_      			=							model_class(_UpperCAmelCase   )

                         @jax.jit
                         def encode_jitted(_UpperCAmelCase      :  Optional[int]		,    _UpperCAmelCase      :  List[str]=None		,    **_UpperCAmelCase      :  Any   ):
                               return model.encode(input_ids=_UpperCAmelCase		,    attention_mask=_UpperCAmelCase   )

                         with self.subTest("JIT Enabled"   ):
                               UpperCAmelCase_      			=							encode_jitted(**_UpperCAmelCase   ).to_tuple()

                         with self.subTest("JIT Disabled"   ):
                               with jax.disable_jit():
                                     UpperCAmelCase_      			=							encode_jitted(**_UpperCAmelCase   ).to_tuple()

                         self.assertEqual(len(_UpperCAmelCase   )		,    len(_UpperCAmelCase   )   )
                         for jitted_output, output in zip(_UpperCAmelCase		,    _UpperCAmelCase   ):
                               self.assertEqual(jitted_output.shape		,    output.shape   )



       def 					lowercase__    (							self      :  Optional[Any]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_			,   UpperCAmelCase_      			=							self.model_tester.prepare_config_and_inputs_for_common()

             for model_class in self.all_model_classes:
                   with self.subTest(model_class.__name__   ):
                         UpperCAmelCase_      			=							model_class(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							model.encode(inputs_dict["input_ids"]		,    inputs_dict["attention_mask"]   )

                         UpperCAmelCase_      			=							{
                             "decoder_input_ids": inputs_dict["decoder_input_ids"],
                             "decoder_attention_mask": inputs_dict["decoder_attention_mask"],
                             "encoder_outputs": encoder_outputs,
                         }

                         @jax.jit
                         def decode_jitted(_UpperCAmelCase      :  Any		,    _UpperCAmelCase      :  int		,    _UpperCAmelCase      :  List[Any]   ):
                               return model.decode(
                                   decoder_input_ids=_UpperCAmelCase		,    decoder_attention_mask=_UpperCAmelCase		,    encoder_outputs=_UpperCAmelCase		,    )

                         with self.subTest("JIT Enabled"   ):
                               UpperCAmelCase_      			=							decode_jitted(**_UpperCAmelCase   ).to_tuple()

                         with self.subTest("JIT Disabled"   ):
                               with jax.disable_jit():
                                     UpperCAmelCase_      			=							decode_jitted(**_UpperCAmelCase   ).to_tuple()

                         self.assertEqual(len(_UpperCAmelCase   )		,    len(_UpperCAmelCase   )   )
                         for jitted_output, output in zip(_UpperCAmelCase		,    _UpperCAmelCase   ):
                               self.assertEqual(jitted_output.shape		,    output.shape   )



       @slow
       def 					lowercase__    (							self      :  List[Any]   )			->						List[str]:


             '''simple docstring'''




             for model_class_name in self.all_model_classes:
                   UpperCAmelCase_      			=							model_class_name.from_pretrained("facebook/blenderbot-400M-distill"   )
                   # FlaxBlenderbotForSequenceClassification expects eos token in input_ids
                   UpperCAmelCase_      			=							np.ones((1, 1)   ) * model.config.eos_token_id
                   UpperCAmelCase_      			=							model(_UpperCAmelCase   )
                   self.assertIsNotNone(_UpperCAmelCase   )







       @unittest.skipUnless(jax_device != "cpu"		,    "3B test too slow on CPU."   )
       @slow
       def 					lowercase__    (							self      :  Union[str, Any]   )			->						str:


             '''simple docstring'''




             UpperCAmelCase_      			=							{"num_beams": 1, "early_stopping": True, "min_length": 15, "max_length": 25}
             UpperCAmelCase_      			=							{"skip_special_tokens": True, "clean_up_tokenization_spaces": True}

             UpperCAmelCase_      			=							FlaxBlenderbotForConditionalGeneration.from_pretrained("facebook/blenderbot-3B"		,    from_pt=_UpperCAmelCase   )
             UpperCAmelCase_      			=							BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"   )

             UpperCAmelCase_      			=							["Sam"]
             UpperCAmelCase_      			=							tokenizer(_UpperCAmelCase		,    return_tensors="jax"   )

             UpperCAmelCase_      			=							model.generate(**_UpperCAmelCase		,    **_UpperCAmelCase   )
             UpperCAmelCase_      			=							"Sam is a great name. It means \"sun\" in Gaelic."

             UpperCAmelCase_      			=							tokenizer.batch_decode(_UpperCAmelCase		,    **_UpperCAmelCase   )
             assert generated_txt[0].strip() == tgt_text









"""simple docstring"""





import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartTokenizer, MBartTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
)

from ...test_tokenization_common import TokenizerTesterMixin


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


if is_torch_available():
  from transformers.models.mbart.modeling_mbart import shift_tokens_right

lowerCamelCase       	=						250_004
lowerCamelCase       	=						250_020
@require_sentencepiece
@require_tokenizers
class 				lowercase__       (   SCREAMING_SNAKE_CASE    , unittest.TestCase				):







       '''simple docstring'''


       UpperCamelCase						=					MBartTokenizer
       UpperCamelCase						=					MBartTokenizerFast
       UpperCamelCase						=					True
       UpperCamelCase						=					True



       def 					lowercase__    (							self      :  int   )			->						Union[str, Any]:


             '''simple docstring'''




             super().setUp()

             # We have a SentencePiece fixture for testing
             UpperCAmelCase_      			=							MBartTokenizer(_UpperCAmelCase		,    keep_accents=_UpperCAmelCase   )
             tokenizer.save_pretrained(self.tmpdirname   )



       def 					lowercase__    (							self      :  Any   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							MBartTokenizer(_UpperCAmelCase		,    keep_accents=_UpperCAmelCase   )

             UpperCAmelCase_      			=							tokenizer.tokenize("This is a test"   )
             self.assertListEqual(_UpperCAmelCase		,    ["▁This", "▁is", "▁a", "▁t", "est"]   )

             self.assertListEqual(
                 tokenizer.convert_tokens_to_ids(_UpperCAmelCase   )		,    [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]]		,    )

             UpperCAmelCase_      			=							tokenizer.tokenize("I was born in 92000, and this is falsé."   )
             self.assertListEqual(
                 _UpperCAmelCase		,    [
                     SPIECE_UNDERLINE + "I",
                     SPIECE_UNDERLINE + "was",
                     SPIECE_UNDERLINE + "b",
                     "or",
                     "n",
                     SPIECE_UNDERLINE + "in",
                     SPIECE_UNDERLINE + "",
                     "9",
                     "2",
                     "0",
                     "0",
                     "0",
                     ",",
                     SPIECE_UNDERLINE + "and",
                     SPIECE_UNDERLINE + "this",
                     SPIECE_UNDERLINE + "is",
                     SPIECE_UNDERLINE + "f",
                     "al",
                     "s",
                     "é",
                     ".",
                 ]		,    )
             UpperCAmelCase_      			=							tokenizer.convert_tokens_to_ids(_UpperCAmelCase   )
             self.assertListEqual(
                 _UpperCAmelCase		,    [
                     value + tokenizer.fairseq_offset
                     for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
                     #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
                 ]		,    )

             UpperCAmelCase_      			=							tokenizer.convert_ids_to_tokens(_UpperCAmelCase   )
             self.assertListEqual(
                 _UpperCAmelCase		,    [
                     SPIECE_UNDERLINE + "I",
                     SPIECE_UNDERLINE + "was",
                     SPIECE_UNDERLINE + "b",
                     "or",
                     "n",
                     SPIECE_UNDERLINE + "in",
                     SPIECE_UNDERLINE + "",
                     "<unk>",
                     "2",
                     "0",
                     "0",
                     "0",
                     ",",
                     SPIECE_UNDERLINE + "and",
                     SPIECE_UNDERLINE + "this",
                     SPIECE_UNDERLINE + "is",
                     SPIECE_UNDERLINE + "f",
                     "al",
                     "s",
                     "<unk>",
                     ".",
                 ]		,    )







       def 					lowercase__    (							self      :  Optional[Any]   )			->						Dict:


             '''simple docstring'''




             if not self.test_slow_tokenizer:
                   # as we don't have a slow version, we can't compare the outputs between slow and fast versions
                   return

             UpperCAmelCase_      			=							(self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart", {})
             for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                   with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""   ):
                         UpperCAmelCase_      			=							self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase		,    **_UpperCAmelCase   )
                         UpperCAmelCase_      			=							self.tokenizer_class.from_pretrained(_UpperCAmelCase		,    **_UpperCAmelCase   )

                         UpperCAmelCase_      			=							tempfile.mkdtemp()

                         UpperCAmelCase_      			=							tokenizer_r.save_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.save_pretrained(_UpperCAmelCase   )

                         # Checks it save with the same files + the tokenizer.json file for the fast one
                         self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )
                         UpperCAmelCase_      			=							tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f   )
                         self.assertSequenceEqual(_UpperCAmelCase		,    _UpperCAmelCase   )

                         # Checks everything loads correctly in the same way
                         UpperCAmelCase_      			=							tokenizer_r.from_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.from_pretrained(_UpperCAmelCase   )

                         # Check special tokens are set accordingly on Rust and Python
                         for key in tokenizer_pp.special_tokens_map:
                               self.assertTrue(hasattr(_UpperCAmelCase		,    _UpperCAmelCase   )   )
                               # self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
                               # self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))

                         shutil.rmtree(_UpperCAmelCase   )

                         # Save tokenizer rust, legacy_format=True
                         UpperCAmelCase_      			=							tempfile.mkdtemp()

                         UpperCAmelCase_      			=							tokenizer_r.save_pretrained(_UpperCAmelCase		,    legacy_format=_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.save_pretrained(_UpperCAmelCase   )

                         # Checks it save with the same files
                         self.assertSequenceEqual(_UpperCAmelCase		,    _UpperCAmelCase   )

                         # Checks everything loads correctly in the same way
                         UpperCAmelCase_      			=							tokenizer_r.from_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.from_pretrained(_UpperCAmelCase   )

                         # Check special tokens are set accordingly on Rust and Python
                         for key in tokenizer_pp.special_tokens_map:
                               self.assertTrue(hasattr(_UpperCAmelCase		,    _UpperCAmelCase   )   )

                         shutil.rmtree(_UpperCAmelCase   )

                         # Save tokenizer rust, legacy_format=False
                         UpperCAmelCase_      			=							tempfile.mkdtemp()

                         UpperCAmelCase_      			=							tokenizer_r.save_pretrained(_UpperCAmelCase		,    legacy_format=_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.save_pretrained(_UpperCAmelCase   )

                         # Checks it saved the tokenizer.json file
                         self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files   )   )

                         # Checks everything loads correctly in the same way
                         UpperCAmelCase_      			=							tokenizer_r.from_pretrained(_UpperCAmelCase   )
                         UpperCAmelCase_      			=							tokenizer_p.from_pretrained(_UpperCAmelCase   )

                         # Check special tokens are set accordingly on Rust and Python
                         for key in tokenizer_pp.special_tokens_map:
                               self.assertTrue(hasattr(_UpperCAmelCase		,    _UpperCAmelCase   )   )

                         shutil.rmtree(_UpperCAmelCase   )



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







       '''simple docstring'''


       UpperCamelCase						=					'''facebook/mbart-large-en-ro'''
       UpperCamelCase						=					[
           ''' UN Chief Says There Is No Military Solution in Syria''',
           ''' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.''',
       ]
       UpperCamelCase						=					[
           '''Şeful ONU declară că nu există o soluţie militară în Siria''',
           '''Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'''
           ''' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'''
           ''' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.''',
       ]
       UpperCamelCase						=					[82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2, EN_CODE]



       @classmethod
       def 					lowercase__    (							cls      :  Optional[Any]   )			->						List[Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							MBartTokenizer.from_pretrained(
                 cls.checkpoint_name		,    src_lang="en_XX"		,    tgt_lang="ro_RO"   )
             UpperCAmelCase_      			=							1
             return cls



       def 					lowercase__    (							self      :  List[Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]		,    250001   )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]		,    250004   )
             self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]		,    250020   )



       def 					lowercase__    (							self      :  Any   )			->						Tuple:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer.batch_encode_plus(self.src_text   ).input_ids[0]
             self.assertListEqual(self.expected_src_tokens		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  Any   )			->						str:


             '''simple docstring'''




             self.assertIn(_UpperCAmelCase		,    self.tokenizer.all_special_ids   )
             UpperCAmelCase_      			=							[RO_CODE, 884, 9019, 96, 9, 916, 86792, 36, 18743, 15596, 5, 2]
             UpperCAmelCase_      			=							self.tokenizer.decode(_UpperCAmelCase		,    skip_special_tokens=_UpperCAmelCase   )
             UpperCAmelCase_      			=							self.tokenizer.decode(generated_ids[1:]		,    skip_special_tokens=_UpperCAmelCase   )
             self.assertEqual(_UpperCAmelCase		,    _UpperCAmelCase   )
             self.assertNotIn(self.tokenizer.eos_token		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  Tuple   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							["this is gunna be a long sentence " * 20]
             assert isinstance(src_text[0]		,    _UpperCAmelCase   )
             UpperCAmelCase_      			=							10
             UpperCAmelCase_      			=							self.tokenizer(_UpperCAmelCase		,    max_length=_UpperCAmelCase		,    truncation=_UpperCAmelCase   ).input_ids[0]
             self.assertEqual(ids[-2]		,    2   )
             self.assertEqual(ids[-1]		,    _UpperCAmelCase   )
             self.assertEqual(len(_UpperCAmelCase   )		,    _UpperCAmelCase   )



       def 					lowercase__    (							self      :  int   )			->						int:


             '''simple docstring'''




             self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]   )		,    [250026, 250001]   )



       def 					lowercase__    (							self      :  Any   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							tempfile.mkdtemp()
             UpperCAmelCase_      			=							self.tokenizer.fairseq_tokens_to_ids
             self.tokenizer.save_pretrained(_UpperCAmelCase   )
             UpperCAmelCase_      			=							MBartTokenizer.from_pretrained(_UpperCAmelCase   )
             self.assertDictEqual(new_tok.fairseq_tokens_to_ids		,    _UpperCAmelCase   )



       @require_torch
       def 					lowercase__    (							self      :  List[Any]   )			->						int:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer(self.src_text		,    text_target=self.tgt_text		,    padding=_UpperCAmelCase		,    return_tensors="pt"   )
             UpperCAmelCase_      			=							shift_tokens_right(batch["labels"]		,    self.tokenizer.pad_token_id   )

             # fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
             assert batch.input_ids[1][-2:].tolist() == [2, EN_CODE]
             assert batch.decoder_input_ids[1][0].tolist() == RO_CODE
             assert batch.decoder_input_ids[1][-1] == 2
             assert batch.labels[1][-2:].tolist() == [2, RO_CODE]



       @require_torch
       def 					lowercase__    (							self      :  Any   )			->						Optional[int]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer(
                 self.src_text		,    text_target=self.tgt_text		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=len(self.expected_src_tokens   )		,    return_tensors="pt"		,    )

             UpperCAmelCase_      			=							shift_tokens_right(batch["labels"]		,    self.tokenizer.pad_token_id   )

             self.assertIsInstance(_UpperCAmelCase		,    _UpperCAmelCase   )

             self.assertEqual((2, 14)		,    batch.input_ids.shape   )
             self.assertEqual((2, 14)		,    batch.attention_mask.shape   )
             UpperCAmelCase_      			=							batch.input_ids.tolist()[0]
             self.assertListEqual(self.expected_src_tokens		,    _UpperCAmelCase   )
             self.assertEqual(2		,    batch.decoder_input_ids[0, -1]   )  # EOS
             # Test that special tokens are reset
             self.assertEqual(self.tokenizer.prefix_tokens		,    []   )
             self.assertEqual(self.tokenizer.suffix_tokens		,    [self.tokenizer.eos_token_id, EN_CODE]   )



       def 					lowercase__    (							self      :  Union[str, Any]   )			->						Union[str, Any]:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer(self.src_text		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=3		,    return_tensors="pt"   )
             UpperCAmelCase_      			=							self.tokenizer(
                 text_target=self.tgt_text		,    padding=_UpperCAmelCase		,    truncation=_UpperCAmelCase		,    max_length=10		,    return_tensors="pt"   )
             UpperCAmelCase_      			=							targets["input_ids"]
             UpperCAmelCase_      			=							shift_tokens_right(_UpperCAmelCase		,    self.tokenizer.pad_token_id   )

             self.assertEqual(batch.input_ids.shape[1]		,    3   )
             self.assertEqual(batch.decoder_input_ids.shape[1]		,    10   )







       @require_torch
       def 					lowercase__    (							self      :  Optional[int]   )			->						Any:


             '''simple docstring'''




             UpperCAmelCase_      			=							self.tokenizer._build_translation_inputs(
                 "A test"		,    return_tensors="pt"		,    src_lang="en_XX"		,    tgt_lang="ar_AR"   )

             self.assertEqual(
                 nested_simplify(_UpperCAmelCase   )		,    {
                     # A, test, EOS, en_XX
                     "input_ids": [[62, 3034, 2, 250004]],
                     "attention_mask": [[1, 1, 1, 1]],
                     # ar_AR
                     "forced_bos_token_id": 250001,
                 }		,    )




