




'''simple docstring'''


import warnings
from typing import Dict

import numpy as np

from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline


if is_tf_available():
  from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
  from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING


def 						SCREAMING_SNAKE_CASE       (      a_	:							Optional[int]			):
  return 1.0 / (1.0 + np.exp(-_outputs			))


def 						SCREAMING_SNAKE_CASE       (      a_	:							List[Any]			):
  __a			=					np.max(_outputs				,					axis=-1				,					keepdims=a_			)
  __a			=					np.exp(_outputs - maxes			)
  return shifted_exp / shifted_exp.sum(axis=-1				,					keepdims=a_			)





class    __lowercase						(  __magic_name__ ):
   _a							=      """sigmoid"""
   _a							=      """softmax"""
   _a							=      """none"""





@add_end_docstrings(
    __magic_name__       ,							R"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """       ,							)
class    __lowercase						(  __magic_name__ ):
   _a							=      False
   _a							=      ClassificationFunction.NONE







   def __init__(	self      ,							**UpperCamelCase      )				->    Union[str, Any]:
     super().__init__(**UpperCamelCase      )

     self.check_model_type(
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
         if self.framework == 'tf'
         else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase=None      ,							UpperCamelCase=None      ,							UpperCamelCase=""      ,							**UpperCamelCase      )				->    int:
     # Using "" as default argument because we're going to use `top_k=None` in user code to declare
     # "No top_k"
     __a			=					tokenizer_kwargs

     __a			=					{}
     if hasattr(self.model.config      ,							'return_all_scores'      ) and return_all_scores is None:
       __a			=					self.model.config.return_all_scores

     if isinstance(UpperCamelCase      ,							UpperCamelCase      ) or top_k is None:
       __a			=					top_k
       __a			=					False
     elif return_all_scores is not None:
       warnings.warn(
           '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
           ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'      ,							UpperCamelCase      ,							)
       if return_all_scores:
         __a			=					None
       else:
         __a			=					1

     if isinstance(UpperCamelCase      ,							UpperCamelCase      ):
       __a			=					ClassificationFunction[function_to_apply.upper()]

     if function_to_apply is not None:
       __a			=					function_to_apply
     return preprocess_params, {}, postprocess_params







   def __call__(	self      ,							*UpperCamelCase      ,							**UpperCamelCase      )				->    Dict:
     __a			=					super().__call__(*UpperCamelCase      ,							**UpperCamelCase      )
     # TODO try and retrieve it in a nicer way from _sanitize_parameters.
     __a			=					'top_k' not in kwargs
     if isinstance(args[0]      ,							UpperCamelCase      ) and _legacy:
       # This pipeline is odd, and return a list when single item is run
       return [result]
     else:
       return result







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							**UpperCamelCase      )				->    Dict[str, GenericTensor]:
     __a			=					self.framework
     if isinstance(UpperCamelCase      ,							UpperCamelCase      ):
       return self.tokenizer(**UpperCamelCase      ,							return_tensors=UpperCamelCase      ,							**UpperCamelCase      )
     elif isinstance(UpperCamelCase      ,							UpperCamelCase      ) and len(UpperCamelCase      ) == 1 and isinstance(inputs[0]      ,							UpperCamelCase      ) and len(inputs[0]      ) == 2:
       # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
       return self.tokenizer(
           text=inputs[0][0]      ,							text_pair=inputs[0][1]      ,							return_tensors=UpperCamelCase      ,							**UpperCamelCase      )
     elif isinstance(UpperCamelCase      ,							UpperCamelCase      ):
       # This is likely an invalid usage of the pipeline attempting to pass text pairs.
       raise ValueError(
           'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
           ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'      )
     return self.tokenizer(UpperCamelCase      ,							return_tensors=UpperCamelCase      ,							**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[Any]:
     return self.model(**UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase=None      ,							UpperCamelCase=1      ,							UpperCamelCase=True      )				->    List[Any]:
     # `_legacy` is used to determine if we're running the naked pipeline and in backward
     # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
     # the more natural result containing the list.
     # Default value before `set_parameters`
     if function_to_apply is None:
       if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
         __a			=					ClassificationFunction.SIGMOID
       elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
         __a			=					ClassificationFunction.SOFTMAX
       elif hasattr(self.model.config      ,							'function_to_apply'      ) and function_to_apply is None:
         __a			=					self.model.config.function_to_apply
       else:
         __a			=					ClassificationFunction.NONE

     __a			=					model_outputs['logits'][0]
     __a			=					outputs.numpy()

     if function_to_apply == ClassificationFunction.SIGMOID:
       __a			=					sigmoid(UpperCamelCase      )
     elif function_to_apply == ClassificationFunction.SOFTMAX:
       __a			=					softmax(UpperCamelCase      )
     elif function_to_apply == ClassificationFunction.NONE:
       __a			=					outputs
     else:
       raise ValueError(f"Unrecognized `function_to_apply` argument: {function_to_apply}"      )

     if top_k == 1 and _legacy:
       return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

     __a			=					[
         {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(UpperCamelCase      )
     ]
     if not _legacy:
       dict_scores.sort(key=lambda UpperCamelCase      : x["score"]      ,							reverse=UpperCamelCase      )
       if top_k is not None:
         __a			=					dict_scores[:top_k]
     return dict_scores









'''simple docstring'''


import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
    get_tests_dir,
    nested_simplify,
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
)

from ...test_tokenization_common import TokenizerTesterMixin


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

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

UpperCAmelCase_ 		=      25_00_04
UpperCAmelCase_ 		=      25_00_20





@require_sentencepiece
@require_tokenizers
class    __lowercase						(  __magic_name__       ,							unittest.TestCase ):
   _a							=      MBartaaTokenizer
   _a							=      MBartaaTokenizerFast
   _a							=      True
   _a							=      True







   def 		UpperCamelCase__			(	self      )				->    Dict:
     super().setUp()

     # We have a SentencePiece fixture for testing
     __a			=					MBartaaTokenizer(UpperCamelCase      ,							src_lang='en_XX'      ,							tgt_lang='ro_RO'      ,							keep_accents=UpperCamelCase      )
     tokenizer.save_pretrained(self.tmpdirname      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					'<s>'
     __a			=					0

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







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					list(self.get_tokenizer().get_vocab().keys()      )

     self.assertEqual(vocab_keys[0]      ,							'<s>'      )
     self.assertEqual(vocab_keys[1]      ,							'<pad>'      )
     self.assertEqual(vocab_keys[-1]      ,							'<mask>'      )
     self.assertEqual(len(UpperCamelCase      )      ,							1054      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     self.assertEqual(self.get_tokenizer().vocab_size      ,							1054      )







   def 		UpperCamelCase__			(	self      )				->    Union[str, Any]:
     __a			=					MBartaaTokenizer(UpperCamelCase      ,							src_lang='en_XX'      ,							tgt_lang='ro_RO'      ,							keep_accents=UpperCamelCase      )

     __a			=					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]]      ,							)

     __a			=					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', '.']      ,							)
     __a			=					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]
         ]      ,							)

     __a			=					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>', '.']      ,							)







   @slow
   def 		UpperCamelCase__			(	self      )				->    Dict:
     # fmt: off
     __a			=					{'input_ids': [[25_0004, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [25_0004, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_0004, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], '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, 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], [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, 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=UpperCamelCase      ,							model_name='facebook/mbart-large-50'      ,							revision='d3913889c59cd5c9e456b269c376325eabad57e2'      ,							)







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

     __a			=					(self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-mbart50', {})
     for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
       with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"      ):
         __a			=					self.rust_tokenizer_class.from_pretrained(UpperCamelCase      ,							**UpperCamelCase      )
         __a			=					self.tokenizer_class.from_pretrained(UpperCamelCase      ,							**UpperCamelCase      )

         __a			=					tempfile.mkdtemp()

         __a			=					tokenizer_r.save_pretrained(UpperCamelCase      )
         __a			=					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      )      )
         __a			=					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
         __a			=					tokenizer_r.from_pretrained(UpperCamelCase      )
         __a			=					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
         __a			=					tempfile.mkdtemp()

         __a			=					tokenizer_r.save_pretrained(UpperCamelCase      ,							legacy_format=UpperCamelCase      )
         __a			=					tokenizer_p.save_pretrained(UpperCamelCase      )

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

         # Checks everything loads correctly in the same way
         __a			=					tokenizer_r.from_pretrained(UpperCamelCase      )
         __a			=					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
         __a			=					tempfile.mkdtemp()

         __a			=					tokenizer_r.save_pretrained(UpperCamelCase      ,							legacy_format=UpperCamelCase      )
         __a			=					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
         __a			=					tokenizer_r.from_pretrained(UpperCamelCase      )
         __a			=					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 ):
   _a							=      """facebook/mbart-large-50-one-to-many-mmt"""
   _a							=      [
       """ 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.""",
   ]
   _a							=      [
       """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.""",
   ]
   _a							=      [EN_CODE, 8274, 127873, 25916, 7, 8622, 2071, 438, 67485, 53, 187895, 23, 51712, 2]







   @classmethod
   def 		UpperCamelCase__			(	cls      )				->    Optional[int]:
     __a			=					MBartaaTokenizer.from_pretrained(
         cls.checkpoint_name      ,							src_lang='en_XX'      ,							tgt_lang='ro_RO'      )
     __a			=					1
     return cls







   def 		UpperCamelCase__			(	self      )				->    int:
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ar_AR']      ,							25_0001      )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['en_EN']      ,							25_0004      )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ro_RO']      ,							25_0020      )
     self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['mr_IN']      ,							25_0038      )







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
     self.assertListEqual(self.expected_src_tokens      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    int:
     self.assertIn(UpperCamelCase      ,							self.tokenizer.all_special_ids      )
     __a			=					[RO_CODE, 884, 9019, 96, 9, 916, 8_6792, 36, 1_8743, 1_5596, 5, 2]
     __a			=					self.tokenizer.decode(UpperCamelCase      ,							skip_special_tokens=UpperCamelCase      )
     __a			=					self.tokenizer.decode(generated_ids[1:]      ,							skip_special_tokens=UpperCamelCase      )
     self.assertEqual(UpperCamelCase      ,							UpperCamelCase      )
     self.assertNotIn(self.tokenizer.eos_token      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					['this is gunna be a long sentence ' * 20]
     assert isinstance(src_text[0]      ,							UpperCamelCase      )
     __a			=					10
     __a			=					self.tokenizer(UpperCamelCase      ,							max_length=UpperCamelCase      ,							truncation=UpperCamelCase      ).input_ids[0]
     self.assertEqual(ids[0]      ,							UpperCamelCase      )
     self.assertEqual(ids[-1]      ,							2      )
     self.assertEqual(len(UpperCamelCase      )      ,							UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    str:
     self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR']      )      ,							[25_0053, 25_0001]      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					tempfile.mkdtemp()
     __a			=					self.tokenizer.fairseq_tokens_to_ids
     self.tokenizer.save_pretrained(UpperCamelCase      )
     __a			=					MBartaaTokenizer.from_pretrained(UpperCamelCase      )
     self.assertDictEqual(new_tok.fairseq_tokens_to_ids      ,							UpperCamelCase      )







   @require_torch
   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					self.tokenizer(self.src_text      ,							text_target=self.tgt_text      ,							padding=UpperCamelCase      ,							return_tensors='pt'      )
     __a			=					shift_tokens_right(batch['labels']      ,							self.tokenizer.pad_token_id      )

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







   @require_torch
   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.tokenizer(
         self.src_text      ,							text_target=self.tgt_text      ,							padding=UpperCamelCase      ,							truncation=UpperCamelCase      ,							max_length=len(self.expected_src_tokens      )      ,							return_tensors='pt'      ,							)
     __a			=					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      )
     __a			=					batch.input_ids.tolist()[0]
     self.assertListEqual(self.expected_src_tokens      ,							UpperCamelCase      )
     self.assertEqual(2      ,							batch.decoder_input_ids[0, 0]      )  # decoder_start_token_id
     # Test that special tokens are reset
     self.assertEqual(self.tokenizer.prefix_tokens      ,							[EN_CODE]      )
     self.assertEqual(self.tokenizer.suffix_tokens      ,							[self.tokenizer.eos_token_id]      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					self.tokenizer(self.src_text      ,							padding=UpperCamelCase      ,							truncation=UpperCamelCase      ,							max_length=3      ,							return_tensors='pt'      )
     __a			=					self.tokenizer(
         text_target=self.tgt_text      ,							padding=UpperCamelCase      ,							truncation=UpperCamelCase      ,							max_length=10      ,							return_tensors='pt'      )
     __a			=					targets['input_ids']
     __a			=					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 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					self.tokenizer._build_translation_inputs(
         'A test'      ,							return_tensors='pt'      ,							src_lang='en_XX'      ,							tgt_lang='ar_AR'      )

     self.assertEqual(
         nested_simplify(UpperCamelCase      )      ,							{
             # en_XX, A, test, EOS
             'input_ids': [[25_0004, 62, 3034, 2]],
             'attention_mask': [[1, 1, 1, 1]],
             # ar_AR
             'forced_bos_token_id': 25_0001,
         }      ,							)



