
"""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


_a      :			Tuple					=							get_tests_dir("""fixtures/test_sentencepiece.model""")

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

_a      :			Tuple					=							250_004
_a      :			str					=							250_020






@require_sentencepiece
@require_tokenizers
class      _UpperCAmelCase				(					_snake_case		,					unittest.TestCase):
    __lowercase							: str  =					MBartaaTokenizer
    __lowercase							: List[Any]  =					MBartaaTokenizerFast
    __lowercase							: Dict  =					True
    __lowercase							: int  =					True
    def 					lowerCamelCase__	(    self     ):
      super().setUp()

      # We have a SentencePiece fixture for testing
      _snake_case     :						List[str]						=   MBartaaTokenizer(snake_case_						,    src_lang="en_XX"						,    tgt_lang="ro_RO"						,    keep_accents=snake_case_     )
      tokenizer.save_pretrained(self.tmpdirname     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   "<s>"
      _snake_case     :						List[Any]						=   0

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(snake_case_     )						,    snake_case_     )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(snake_case_     )						,    snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   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(snake_case_     )						,    10_54     )
    def 					lowerCamelCase__	(    self     ):
      self.assertEqual(self.get_tokenizer().vocab_size						,    10_54     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Any						=   MBartaaTokenizer(snake_case_						,    src_lang="en_XX"						,    tgt_lang="ro_RO"						,    keep_accents=snake_case_     )

      _snake_case     :						Any						=   tokenizer.tokenize("This is a test"     )
      self.assertListEqual(snake_case_						,    ["▁This", "▁is", "▁a", "▁t", "est"]     )

      self.assertListEqual(
          tokenizer.convert_tokens_to_ids(snake_case_     )						,    [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]]						,    )

      _snake_case     :						Optional[Any]						=   tokenizer.tokenize("I was born in 92000, and this is falsé."     )
      self.assertListEqual(
          snake_case_						,    [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", "."]						,    )
      _snake_case     :						Dict						=   tokenizer.convert_tokens_to_ids(snake_case_     )
      self.assertListEqual(
          snake_case_						,    [
              value + tokenizer.fairseq_offset
              for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
          ]						,    )

      _snake_case     :						Optional[Any]						=   tokenizer.convert_ids_to_tokens(snake_case_     )
      self.assertListEqual(
          snake_case_						,    [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 					lowerCamelCase__	(    self     ):
      # fmt: off
      _snake_case     :						Tuple						=   {"input_ids": [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 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_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 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=snake_case_						,    model_name="facebook/mbart-large-50"						,    revision="d3913889c59cd5c9e456b269c376325eabad57e2"						,    )


    def 					lowerCamelCase__	(    self     ):
      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

      _snake_case     :						List[str]						=   (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})'     ):
          _snake_case     :						Dict						=   self.rust_tokenizer_class.from_pretrained(snake_case_						,    **snake_case_     )
          _snake_case     :						List[str]						=   self.tokenizer_class.from_pretrained(snake_case_						,    **snake_case_     )

          _snake_case     :						int						=   tempfile.mkdtemp()

          _snake_case     :						Union[str, Any]						=   tokenizer_r.save_pretrained(snake_case_     )
          _snake_case     :						Union[str, Any]						=   tokenizer_p.save_pretrained(snake_case_     )

          # 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     )     )
          _snake_case     :						Dict						=   tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f     )
          self.assertSequenceEqual(snake_case_						,    snake_case_     )

          # Checks everything loads correctly in the same way
          _snake_case     :						Tuple						=   tokenizer_r.from_pretrained(snake_case_     )
          _snake_case     :						Tuple						=   tokenizer_p.from_pretrained(snake_case_     )

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

          shutil.rmtree(snake_case_     )

          # Save tokenizer rust, legacy_format=True
          _snake_case     :						int						=   tempfile.mkdtemp()

          _snake_case     :						List[Any]						=   tokenizer_r.save_pretrained(snake_case_						,    legacy_format=snake_case_     )
          _snake_case     :						Any						=   tokenizer_p.save_pretrained(snake_case_     )

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

          # Checks everything loads correctly in the same way
          _snake_case     :						List[Any]						=   tokenizer_r.from_pretrained(snake_case_     )
          _snake_case     :						Any						=   tokenizer_p.from_pretrained(snake_case_     )

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

          shutil.rmtree(snake_case_     )

          # Save tokenizer rust, legacy_format=False
          _snake_case     :						Tuple						=   tempfile.mkdtemp()

          _snake_case     :						Optional[int]						=   tokenizer_r.save_pretrained(snake_case_						,    legacy_format=snake_case_     )
          _snake_case     :						int						=   tokenizer_p.save_pretrained(snake_case_     )

          # 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
          _snake_case     :						List[str]						=   tokenizer_r.from_pretrained(snake_case_     )
          _snake_case     :						List[str]						=   tokenizer_p.from_pretrained(snake_case_     )

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

          shutil.rmtree(snake_case_     )






@require_torch
@require_sentencepiece
@require_tokenizers
class      _UpperCAmelCase				(					unittest.TestCase):
    __lowercase							: int  =					"""facebook/mbart-large-50-one-to-many-mmt"""
    __lowercase							: List[str]  =					[
        """ 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.""",
    ]
    __lowercase							: Any  =					[
        """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.""",
    ]
    __lowercase							: List[Any]  =					[EN_CODE, 8_2_7_4, 1_2_7_8_7_3, 2_5_9_1_6, 7, 8_6_2_2, 2_0_7_1, 4_3_8, 6_7_4_8_5, 5_3, 1_8_7_8_9_5, 2_3, 5_1_7_1_2, 2]
    @classmethod
    def 					lowerCamelCase__	(    cls     ):
      _snake_case     :						MBartaaTokenizer						=   MBartaaTokenizer.from_pretrained(
          cls.checkpoint_name						,    src_lang="en_XX"						,    tgt_lang="ro_RO"     )
      _snake_case     :						Any						=   1
      return cls
    def 					lowerCamelCase__	(    self     ):
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"]						,    25_00_01     )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"]						,    25_00_04     )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"]						,    25_00_20     )
      self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"]						,    25_00_38     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[Any]						=   self.tokenizer.batch_encode_plus(self.src_text     ).input_ids[0]
      self.assertListEqual(self.expected_src_tokens						,    snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      self.assertIn(snake_case_						,    self.tokenizer.all_special_ids     )
      _snake_case     :						Optional[int]						=   [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
      _snake_case     :						int						=   self.tokenizer.decode(snake_case_						,    skip_special_tokens=snake_case_     )
      _snake_case     :						List[Any]						=   self.tokenizer.decode(generated_ids[1:]						,    skip_special_tokens=snake_case_     )
      self.assertEqual(snake_case_						,    snake_case_     )
      self.assertNotIn(self.tokenizer.eos_token						,    snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Optional[Any]						=   ["this is gunna be a long sentence " * 20]
      assert isinstance(src_text[0]						,    snake_case_     )
      _snake_case     :						Tuple						=   10
      _snake_case     :						Tuple						=   self.tokenizer(snake_case_						,    max_length=snake_case_						,    truncation=snake_case_     ).input_ids[0]
      self.assertEqual(ids[0]						,    snake_case_     )
      self.assertEqual(ids[-1]						,    2     )
      self.assertEqual(len(snake_case_     )						,    snake_case_     )
    def 					lowerCamelCase__	(    self     ):
      self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"]     )						,    [25_00_53, 25_00_01]     )
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						Union[str, Any]						=   tempfile.mkdtemp()
      _snake_case     :						Tuple						=   self.tokenizer.fairseq_tokens_to_ids
      self.tokenizer.save_pretrained(snake_case_     )
      _snake_case     :						List[str]						=   MBartaaTokenizer.from_pretrained(snake_case_     )
      self.assertDictEqual(new_tok.fairseq_tokens_to_ids						,    snake_case_     )
    @require_torch
    def 					lowerCamelCase__	(    self     ):
      _snake_case     :						List[Any]						=   self.tokenizer(self.src_text						,    text_target=self.tgt_text						,    padding=snake_case_						,    return_tensors="pt"     )
      _snake_case     :						Any						=   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 					lowerCamelCase__	(    self     ):
      _snake_case     :						int						=   self.tokenizer(
          self.src_text						,    text_target=self.tgt_text						,    padding=snake_case_						,    truncation=snake_case_						,    max_length=len(self.expected_src_tokens     )						,    return_tensors="pt"						,    )
      _snake_case     :						str						=   shift_tokens_right(batch["labels"]						,    self.tokenizer.pad_token_id     )

      self.assertIsInstance(snake_case_						,    snake_case_     )

      self.assertEqual((2, 14)						,    batch.input_ids.shape     )
      self.assertEqual((2, 14)						,    batch.attention_mask.shape     )
      _snake_case     :						Optional[Any]						=   batch.input_ids.tolist()[0]
      self.assertListEqual(self.expected_src_tokens						,    snake_case_     )
      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 					lowerCamelCase__	(    self     ):
      _snake_case     :						Dict						=   self.tokenizer(self.src_text						,    padding=snake_case_						,    truncation=snake_case_						,    max_length=3						,    return_tensors="pt"     )
      _snake_case     :						Any						=   self.tokenizer(
          text_target=self.tgt_text						,    padding=snake_case_						,    truncation=snake_case_						,    max_length=10						,    return_tensors="pt"     )
      _snake_case     :						List[Any]						=   targets["input_ids"]
      _snake_case     :						List[Any]						=   shift_tokens_right(snake_case_						,    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 					lowerCamelCase__	(    self     ):
      _snake_case     :						Union[str, Any]						=   self.tokenizer._build_translation_inputs(
          "A test"						,    return_tensors="pt"						,    src_lang="en_XX"						,    tgt_lang="ar_AR"     )

      self.assertEqual(
          nested_simplify(snake_case_     )						,    {
              # en_XX, A, test, EOS
              "input_ids": [[25_00_04, 62, 30_34, 2]],
              "attention_mask": [[1, 1, 1, 1]],
              # ar_AR
              "forced_bos_token_id": 25_00_01,
          }						,    )



"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a      :			Optional[int]					=							logging.get_logger(__name__)

_a      :			str					=							{"""openai-gpt""": """https://huggingface.co/openai-gpt/resolve/main/config.json"""}






class      _UpperCAmelCase				(					_snake_case):
    __lowercase							: Optional[Any]  =					"""openai-gpt"""
    __lowercase							: Dict  =					{
        """max_position_embeddings""": """n_positions""",
        """hidden_size""": """n_embd""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }
    def __init__(    self						,    snake_case_=4_04_78						,    snake_case_=5_12						,    snake_case_=7_68						,    snake_case_=12						,    snake_case_=12						,    snake_case_="gelu"						,    snake_case_=0.1						,    snake_case_=0.1						,    snake_case_=0.1						,    snake_case_=1E-5						,    snake_case_=0.02						,    snake_case_="cls_index"						,    snake_case_=True						,    snake_case_=None						,    snake_case_=True						,    snake_case_=0.1						,    **snake_case_						,    ):
      _snake_case     :						Tuple						=   vocab_size
      _snake_case     :						Dict						=   n_positions
      _snake_case     :						Any						=   n_embd
      _snake_case     :						Any						=   n_layer
      _snake_case     :						Optional[int]						=   n_head
      _snake_case     :						Union[str, Any]						=   afn
      _snake_case     :						Dict						=   resid_pdrop
      _snake_case     :						str						=   embd_pdrop
      _snake_case     :						Union[str, Any]						=   attn_pdrop
      _snake_case     :						str						=   layer_norm_epsilon
      _snake_case     :						Union[str, Any]						=   initializer_range
      _snake_case     :						Any						=   summary_type
      _snake_case     :						List[str]						=   summary_use_proj
      _snake_case     :						Optional[int]						=   summary_activation
      _snake_case     :						Union[str, Any]						=   summary_first_dropout
      _snake_case     :						Optional[int]						=   summary_proj_to_labels
      super().__init__(**snake_case_     )

