





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


_A :   str  			=  get_tests_dir('fixtures/test_sentencepiece.model')


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

_A :   List[Any]  			=  25_00_04
_A :   Dict  			=  25_00_20


@require_sentencepiece
@require_tokenizers
class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,unittest.TestCase ):
       _UpperCAmelCase				:  Dict			   =   MBartTokenizer
       _UpperCAmelCase				:  str			   =   MBartTokenizerFast
       _UpperCAmelCase				:  List[Any]			   =   True
       _UpperCAmelCase				:  Dict			   =   True







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->List[str]:
          super().setUp()

          # We have a SentencePiece fixture for testing
          lowerCamelCase__       :						Dict        =							MBartTokenizer(A		, keep_accents=A      )
          tokenizer.save_pretrained(self.tmpdirname      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->List[Any]:
          lowerCamelCase__       :						Dict        =							MBartTokenizer(A		, keep_accents=A      )

          lowerCamelCase__       :						Dict        =							tokenizer.tokenize('''This is a test'''      )
          self.assertListEqual(A		, ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']      )

          self.assertListEqual(
              tokenizer.convert_tokens_to_ids(A      )		, [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]		, )

          lowerCamelCase__       :						int        =							tokenizer.tokenize('''I was born in 92000, and this is falsé.'''      )
          self.assertListEqual(
              A		, [
                  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''',
                  '''.''',
              ]		, )
          lowerCamelCase__       :						int        =							tokenizer.convert_tokens_to_ids(A      )
          self.assertListEqual(
              A		, [
                  value + tokenizer.fairseq_offset
                  for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
                  #                                       ^ unk: 2 + 1 = 3                  unk: 2 + 1 = 3 ^
              ]		, )

          lowerCamelCase__       :						Optional[int]        =							tokenizer.convert_ids_to_tokens(A      )
          self.assertListEqual(
              A		, [
                  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 						__lowerCamelCase   (				self					:     List[str]      )       ->List[str]:
          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

          lowerCamelCase__       :						Any        =							(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})"      ):
                lowerCamelCase__       :						Tuple        =							self.rust_tokenizer_class.from_pretrained(A		, **A      )
                lowerCamelCase__       :						str        =							self.tokenizer_class.from_pretrained(A		, **A      )

                lowerCamelCase__       :						Dict        =							tempfile.mkdtemp()

                lowerCamelCase__       :						str        =							tokenizer_r.save_pretrained(A      )
                lowerCamelCase__       :						Optional[Any]        =							tokenizer_p.save_pretrained(A      )

                # 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      )      )
                lowerCamelCase__       :						Union[str, Any]        =							tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f      )
                self.assertSequenceEqual(A		, A      )

                # Checks everything loads correctly in the same way
                lowerCamelCase__       :						Optional[int]        =							tokenizer_r.from_pretrained(A      )
                lowerCamelCase__       :						List[Any]        =							tokenizer_p.from_pretrained(A      )

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

                shutil.rmtree(A      )

                # Save tokenizer rust, legacy_format=True
                lowerCamelCase__       :						Optional[int]        =							tempfile.mkdtemp()

                lowerCamelCase__       :						Optional[int]        =							tokenizer_r.save_pretrained(A		, legacy_format=A      )
                lowerCamelCase__       :						int        =							tokenizer_p.save_pretrained(A      )

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

                # Checks everything loads correctly in the same way
                lowerCamelCase__       :						List[str]        =							tokenizer_r.from_pretrained(A      )
                lowerCamelCase__       :						Optional[int]        =							tokenizer_p.from_pretrained(A      )

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

                shutil.rmtree(A      )

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

                lowerCamelCase__       :						Optional[Any]        =							tokenizer_r.save_pretrained(A		, legacy_format=A      )
                lowerCamelCase__       :						List[Any]        =							tokenizer_p.save_pretrained(A      )

                # 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
                lowerCamelCase__       :						Any        =							tokenizer_r.from_pretrained(A      )
                lowerCamelCase__       :						List[Any]        =							tokenizer_p.from_pretrained(A      )

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

                shutil.rmtree(A      )


@require_torch
@require_sentencepiece
@require_tokenizers
class        __SCREAMING_SNAKE_CASE      (			unittest.TestCase ):
       _UpperCAmelCase				:  List[Any]			   =   "facebook/mbart-large-en-ro"
       _UpperCAmelCase				:  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.",
       ]
       _UpperCAmelCase				:  Tuple			   =   [
           "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				:  Any			   =   [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, EN_CODE]







       @classmethod
       def 						__lowerCamelCase   (				cls					:     Dict      )       ->Tuple:
          lowerCamelCase__       :						MBartTokenizer        =							MBartTokenizer.from_pretrained(
              cls.checkpoint_name		, src_lang='''en_XX'''		, tgt_lang='''ro_RO'''      )
          lowerCamelCase__       :						Dict        =							1
          return cls







       def 						__lowerCamelCase   (				self					:     Dict      )       ->Optional[int]:
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ar_AR''']		, 2_5_0_0_0_1      )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''en_EN''']		, 2_5_0_0_0_4      )
          self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['''ro_RO''']		, 2_5_0_0_2_0      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Dict:
          lowerCamelCase__       :						int        =							self.tokenizer.batch_encode_plus(self.src_text      ).input_ids[0]
          self.assertListEqual(self.expected_src_tokens		, A      )







       def 						__lowerCamelCase   (				self					:     Any      )       ->Dict:
          self.assertIn(A		, self.tokenizer.all_special_ids      )
          lowerCamelCase__       :						List[str]        =							[RO_CODE, 8_8_4, 9_0_1_9, 9_6, 9, 9_1_6, 8_6_7_9_2, 3_6, 1_8_7_4_3, 1_5_5_9_6, 5, 2]
          lowerCamelCase__       :						Dict        =							self.tokenizer.decode(A		, skip_special_tokens=A      )
          lowerCamelCase__       :						Union[str, Any]        =							self.tokenizer.decode(generated_ids[1:]		, skip_special_tokens=A      )
          self.assertEqual(A		, A      )
          self.assertNotIn(self.tokenizer.eos_token		, A      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          lowerCamelCase__       :						List[str]        =							['''this is gunna be a long sentence ''' * 2_0]
          assert isinstance(src_text[0]		, A      )
          lowerCamelCase__       :						Optional[int]        =							1_0
          lowerCamelCase__       :						Optional[int]        =							self.tokenizer(A		, max_length=A		, truncation=A      ).input_ids[0]
          self.assertEqual(ids[-2]		, 2      )
          self.assertEqual(ids[-1]		, A      )
          self.assertEqual(len(A      )		, A      )







       def 						__lowerCamelCase   (				self					:     Union[str, Any]      )       ->Dict:
          self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['''<mask>''', '''ar_AR''']      )		, [2_5_0_0_2_6, 2_5_0_0_0_1]      )







       def 						__lowerCamelCase   (				self					:     int      )       ->Tuple:
          lowerCamelCase__       :						Union[str, Any]        =							tempfile.mkdtemp()
          lowerCamelCase__       :						Dict        =							self.tokenizer.fairseq_tokens_to_ids
          self.tokenizer.save_pretrained(A      )
          lowerCamelCase__       :						int        =							MBartTokenizer.from_pretrained(A      )
          self.assertDictEqual(new_tok.fairseq_tokens_to_ids		, A      )







       @require_torch
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->str:
          lowerCamelCase__       :						int        =							self.tokenizer(self.src_text		, text_target=self.tgt_text		, padding=A		, return_tensors='''pt'''      )
          lowerCamelCase__       :						int        =							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 						__lowerCamelCase   (				self					:     List[Any]      )       ->Optional[int]:
          lowerCamelCase__       :						List[str]        =							self.tokenizer(
              self.src_text		, text_target=self.tgt_text		, padding=A		, truncation=A		, max_length=len(self.expected_src_tokens      )		, return_tensors='''pt'''		, )

          lowerCamelCase__       :						Optional[Any]        =							shift_tokens_right(batch['''labels''']		, self.tokenizer.pad_token_id      )

          self.assertIsInstance(A		, A      )

          self.assertEqual((2, 1_4)		, batch.input_ids.shape      )
          self.assertEqual((2, 1_4)		, batch.attention_mask.shape      )
          lowerCamelCase__       :						str        =							batch.input_ids.tolist()[0]
          self.assertListEqual(self.expected_src_tokens		, A      )
          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 						__lowerCamelCase   (				self					:     Tuple      )       ->Dict:
          lowerCamelCase__       :						Any        =							self.tokenizer(self.src_text		, padding=A		, truncation=A		, max_length=3		, return_tensors='''pt'''      )
          lowerCamelCase__       :						Tuple        =							self.tokenizer(
              text_target=self.tgt_text		, padding=A		, truncation=A		, max_length=1_0		, return_tensors='''pt'''      )
          lowerCamelCase__       :						Optional[int]        =							targets['''input_ids''']
          lowerCamelCase__       :						List[str]        =							shift_tokens_right(A		, self.tokenizer.pad_token_id      )

          self.assertEqual(batch.input_ids.shape[1]		, 3      )
          self.assertEqual(batch.decoder_input_ids.shape[1]		, 1_0      )







       @require_torch
       def 						__lowerCamelCase   (				self					:     Optional[int]      )       ->str:
          lowerCamelCase__       :						str        =							self.tokenizer._build_translation_inputs(
              '''A test'''		, return_tensors='''pt'''		, src_lang='''en_XX'''		, tgt_lang='''ar_AR'''      )

          self.assertEqual(
              nested_simplify(A      )		, {
                  # A, test, EOS, en_XX
                  '''input_ids''': [[6_2, 3_0_3_4, 2, 2_5_0_0_0_4]],
                  '''attention_mask''': [[1, 1, 1, 1]],
                  # ar_AR
                  '''forced_bos_token_id''': 2_5_0_0_0_1,
              }		, )










from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_A :   Tuple  			=  logging.get_logger(__name__)

_A :   List[str]  			=  {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}


class        __SCREAMING_SNAKE_CASE      (			lowerCAmelCase_					,lowerCAmelCase_ ):
       _UpperCAmelCase				:  Dict			   =   "nat"

       _UpperCAmelCase				:  Tuple			   =   {
           "num_attention_heads": "num_heads",
           "num_hidden_layers": "num_layers",
       }







       def __init__(				self					:     List[Any]		, A					:     Union[str, Any]=4		, A					:     str=3		, A					:     List[Any]=6_4		, A					:     Optional[Any]=[3, 4, 6, 5]		, A					:     int=[2, 4, 8, 1_6]		, A					:     Optional[int]=7		, A					:     List[Any]=3.0		, A					:     str=True		, A					:     str=0.0		, A					:     Any=0.0		, A					:     int=0.1		, A					:     Tuple="gelu"		, A					:     List[Any]=0.02		, A					:     str=1e-5		, A					:     Optional[int]=0.0		, A					:     Optional[Any]=None		, A					:     Dict=None		, **A					:     str		, )       ->Union[str, Any]:
          super().__init__(**A      )

          lowerCamelCase__       :						Any        =							patch_size
          lowerCamelCase__       :						Optional[Any]        =							num_channels
          lowerCamelCase__       :						Any        =							embed_dim
          lowerCamelCase__       :						str        =							depths
          lowerCamelCase__       :						Union[str, Any]        =							len(A      )
          lowerCamelCase__       :						int        =							num_heads
          lowerCamelCase__       :						Optional[int]        =							kernel_size
          lowerCamelCase__       :						Optional[int]        =							mlp_ratio
          lowerCamelCase__       :						List[Any]        =							qkv_bias
          lowerCamelCase__       :						Tuple        =							hidden_dropout_prob
          lowerCamelCase__       :						List[str]        =							attention_probs_dropout_prob
          lowerCamelCase__       :						List[Any]        =							drop_path_rate
          lowerCamelCase__       :						Union[str, Any]        =							hidden_act
          lowerCamelCase__       :						Union[str, Any]        =							layer_norm_eps
          lowerCamelCase__       :						List[Any]        =							initializer_range
          # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          lowerCamelCase__       :						str        =							int(embed_dim * 2 ** (len(A      ) - 1)      )
          lowerCamelCase__       :						Dict        =							layer_scale_init_value
          lowerCamelCase__       :						Dict        =							['''stem'''] + [F"stage{idx}" for idx in range(1		, len(A      ) + 1      )]
          lowerCamelCase__       ,							lowerCamelCase__       :						str        =							get_aligned_output_features_output_indices(
              out_features=A		, out_indices=A		, stage_names=self.stage_names      )



